Computational ecology: Artificial neural networks and their applications

Computational Ecology: Artificial Neural Networks and their Applications

Volumn , Issue , 2010, Pages 1-296

  Zhang, Wen Jun ^a  

^a SUN YAT SEN UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

APPLICATION PROGRAMS; APPROXIMATION ALGORITHMS; DATA MINING; MATLAB; NEURAL NETWORKS; STUDENTS; TEACHING;

COMPUTATIONAL ECOLOGY; COMPUTATIONAL SCIENCE; CONVENTIONAL METHODS; ECOLOGICAL PROBLEM; ENVIRONMENTAL SCIENCE; ENVIRONMENTAL SCIENTISTS; FUNCTION APPROXIMATION; UNIVERSITY TEACHERS;

ECOLOGY;

EID: 84967584876     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1142/7436     Document Type: Book

References (301)

1. Abdel-Aal RE. Hourly temperature forecasting using abductive networks. Engineering Applications of Artificial Intelligence, 17: 543-556, 2004.
2. Abrahart RJ, White SM. Modelling sediment transfer in Malawi: Comparing backpropagation neural network solutions against a multiple linear regression benchmark using small data set. Physics and Chemistry of the Earth (B), 26(1): 19-24, 2001.
3. Acharya C, Mohanty S, Sukla LB et al.Prediction of sulphur removal with Acidithiobacillus sp. using artificial neural networks. Ecological Modelling, 190: 223-230, 2006.
4. Agirre-Basurko E, Ibarra-Berastegi G, Madariaga I. Regression and multilayer perceptronbased models to forecast hourly O3 and NO2 levels in the Bilbao area. Environmental Modelling & Software, 21(4): 430-446, 2006.
5. Aires F et al.A regularized neural net approach for retrieval of atmospheric and surface temperature with the IASI instrument. Journal of Applied Meteorology, 41: 144-159, 2002.
6. Albus JS. A theory of cerebellar function. Mathematical Biosciences, 10: 25-61, 1971.
7. Almasri MN, Kaluarachchi JJ. Modular neural networks to predict the nitrate distribution in ground water using the on-ground nitrogen loading and recharge data. EnvironmentalModelling and Software, 20: 851-871, 2005.
8. Altieri MA. Biodiversity and Pest Management in Agroecosystems. Haworth Press, New York, USA, 1994.
9. Altieri MA. Agroecology: The Science of Sustainable Agriculture.Westview Press, Boulder, Colorado, USA, 1995.
10. Altieri MA, Letourneau DK. Vegetation diversity and insect pest outbreaks. CRC Critical Review in Plant Science, 2: 131-169, 1984.
11. Amari SI. Field theory of self-organizing neural nets. IEEE Transactions on Systems, Man and Cybernetics, 13: 741-748, 1983.
12. Amari SI. Mathematical foundation of Neurocomputing. Proceedings of the IEEE on Neural Networks, 78: 1443-1463, 1990.
13. Amari S. Differential Geometrical Methods in Statistics. Springer Lecture Notes in Statistics Vol. 28. Springer-Verlag, Berlin, Germany, 1985.
14. Amari S. Differential geometry in statistical inference. Proc. ISI, 46th Session of the ISI, 52(2): 321-338, 1987.
15. Amari S. Differential geometry of a parametric family of invertible linear systems-Rimannian metric, dual affine connections and divergence. Mathematical Systems Theory, 20: 53-82, 1987.
16. Amari S. On mathematical methods in the theory of neural networks. Proc. 1st IEEE ICNN, Vol. 3, pp. IU3-Ell10, 1987.
17. Amari S. Information geometry of EM and EM algorithm for neural networks. Neural Networks, 8(9): 1379-1408.
18. Amari S. The natural gradient learning algorithm for neural networks. Theoretical Aspects of Neural Computation — A Multidisciplinary Perspective, Wong KYM, King I, Yeung DY (Eds.). Hong Kong InternationalWorkshop. Springer, 1998.
19. Amari S, Murata N, Muller K, Finke M., Yang HH.Asymptotic statistical theory of overtraining and cross-validation. IEEE Transactions on Neural Networks, 85: 985-996, 1997.
20. Anderson JA. A simple neural network generating an interactive memory. Mathematical Biosciences, 14: 197-220, 1972.
21. Anderson JA, Rosenfeld E. Neurocomputing: Foundations of Research. MIT Press, Cambridge, USA, 1989.
22. Andersen MC. Potential applications of population viability analysis to risk assessment for invasive species. Human and Ecological Risk Assessment, 11: 1083-1095, 2005.
23. Andow DA. Vegetational diversity and arthropod population response. Annual Review of Entomology, 36: 561-586, 1991.
24. Anthony M, Biggs N. Computational Learning Theory. Cambridge University Press, Boston, USA, 1992.
25. Baker R, Cannon R, Bartlett P, Barker I. Novel strategies for assessing and managing the risks posed by invasive alien species to global crop production and biodiversity. Annals of Applied Biology, 146: 177-191, 2005.
26. Ballester EB, Valls GCI, Carrasco-Rodriguez JL et al.Effective 1-day ahead prediction of hourly surface ozone concentrations in eastern Spain using linear models and neural networks. Ecological Modelling, 156: 27-41, 2002.
27. Battiti R. First and second order methods for learning between steepest descent and Newton’s method. Neural Computation, 4: 141-166, 1992.
28. Berry T, Linoff J. Data Mining Techniques. JohnWiley and Sons, NewYork, USA, 1997.
29. Bian ZQ, Zhang XG. Pattern Recognition(2nd Edition). Tsinghua University Press, Beijing, China, 2000.
30. Bork EW, Hudson RJ, Bailey AW. Upland plant community classification in Elk Island National Park, Alberta, Canada, using disturbance history and physical site factors. Plant Ecology, 130: 171-190, 1997.
31. Bradshaw CJA, Davis LS, Purvis M et al.Using artificial neural networks to model the suitability of coastline for breeding by New Zealand fur seals (Arctocephalus forsteri). Ecological Modelling, 148: 111-131, 2002.
32. Breiman L. Statistical modeling: The two cultures (with discussion). Statistical Science, 16: 199-231, 2001.
33. Broomhead DS, Lowe D. Multivariable functional interpolation and adaptive networks. Complex Systems, 2: 321-355, 1988.
34. Brown KS Jr. Conservation of neotropical insects: Insects as indicators. The Conservation of Insects and Their Habitats, Collins NM, Thomas JA (Eds.). Academic Press, London, England, 1991.
35. Bunge J, Fitzpatrick M. Estimating the number of species: A review. Journal of American Statistician Association, 88: 364-373, 1993.
36. Burden R, Faires JD. Numerical Analysis(7th Edition). Thomson Learning, Inc., NewYork, USA, 2001.
37. Burnham KP, Overton WS. Estimation of the size of a closed population when capture probabilities vary among animals. Biometrika, 65: 623-633, 1978.
38. Burnham KP, Overton WS. Robust estimation of population when capture probabilities vary among animals. Ecology, 60: 927-936, 1979.
39. Cardelino C, Chang M, John J et al. Ozone predictions in Atlanta, Georgia: Analysis of the 1999 ozone season. Journal of the Air and Waste Management Association, 51: 1227-1236, 2001.
40. Carpenter GA, Grossberg S.Amassive parallel architecture for a self-organing neural pattern recognition machine. Computer Vision, Graphics, and Image Processing, 37: 54-115, 1987.
41. Carpenter GA, Grossberg S. ART2: Self-organzation of stable category recognition codes for analog input patterns. Applied Optics, 26(23): 4019-4930, 1987.
42. Carpenter GA, Grossberg S. ART3: Hierachical search using chemical transmitters in selforganizing pattern recognition architectures. Neural Networks, 3(23): 129-152, 1990.
43. Carpenter GA, Grossberg S, Reynolds J. ARTMAP: Supervised real-time learning and classification of nonstationary data by a self-organizing neural network. Neural Networks, 4(5): 169-181, 1991.
44. Cassmassi JC. Objective ozone forecasting in the South CoastAir Basin: Updating the objective prediction models for the late 1990s and southern California ozone study (SCOS97-NARSTO) application. Proceedings of the 12th Conference on Numerical Weather Prediction 54-58, American Meteorology Society, Boston, MA, USA, 1998.
45. Castellano G, Fanelli AM, Pelillo M. An iterative pruning algorithm for feedforward neural networks. IEEE Transactions on Neural Networks, 8(3): 519-531, 1997.
46. Cereghino R, Giraudel JL, Compin A. Spatial analysis of stream invertebrates distribution in the Adour-Garonne drainage basin (France), using Kohonen self organizing maps. Ecological Modelling, 146: 167-180, 2001.
47. Chao A. Non-parametric estimation of the number of classes in a population. Scandinavian Journal of Statistics, 11: 265-270, 1984.
48. Chao A, Lee SM. Estimating the number of classes via sample coverage. Journal of American Statistician Association, 87: 210-217, 1992.
49. Chapin FS. Effects of plant traits on ecosystem and regional processes:A conceptual framework for predicting the consequences of global change. Annals Botany, 91: 1-9, 2003.
50. Chapin FS, Sala OE, Huber-Sannwald E. Global Biodiversity in a Changing Environment: Scenarios for the 21st Century. Springer-Verlag, NewYork, USA, 2001.
51. Charalambous C. Conjugate gradient algorithm for efficient training of artificial neural networks. IEEE Proceedings G, 139(3): 301-310, 1992.
52. Chen JX. Course Notes on Algebraic Topology. Higher Education Press, Beijing, China, 1987.
53. Chen LZ, Ma KP. Biodiversity Science: Principles and Practices. Shanghai Science and Technology Press, Shanghai, China, 2001.
54. Chen XS, Chen WH. Course Notes on Differential Geometry. Beijing University Press, Beijing, China, 1980.
55. Chen XR et al.Modern Regression Analysis. Anhui Education Press, Hefei, China, 1987.
56. Chen K, Xu L, Chi H. Improved learning algorithms for mixture of experts in multiclass classification. Neural Networks, 12(9): 1229-1252, 1999.
57. Cherkassky V, Lari-Najafi H. Constrained topological mapping for nonparametric regression analysis. Neural Networks, 4: 27-40, 1991.
58. Churing Y. Backpropagation, Theory, Architecture and Applications. Lawrence Erbaum Publishers, NewYork, USA, 1995.
59. Cohen ACJ. Simplified estimators for the normal distribution when samples are singly censored sample. Technometrics, 1: 217-237, 1959.
60. Cohen ACJ. Tables for maximum likelihood estimates: Singly truncated and singly censored sample. Technometrics, 3: 535-541, 1961.
61. Cohen JE. Food webs and niche space. Monographs in Population Biology11, Princeton University Press, Princeton, USA, 1978.
62. Cohen JE et al.Improving food webs. Ecology, 74: 252-258, 1993.
63. Cohen J. A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 18(20): 3746, 1960.
64. Coleman BD, Mares MA, Willig MR, HsiehYH. Randomness, area, and species richness. Ecology, 63: 1121-1133, 1982.
65. Cowell RK. Human aspects of biodiversity: An evolutionary perspective. Biological Diversity and Global Change, Solbrig OT, van Emden HM, van Oordt PGWJ (Eds.). International Union of Biological Sciences, Monograph No. 8. IUBS Press, Paris, France, 1992.
66. Colwell RK, Coddington JA. Estimating terrestrial biodiversity through extrapolation. Philosophical Transactions of the Royal Society London B, 345: 101-108, 1994.
67. Dempster AP, Laird NM, Rubin DB. Maximum likelihood from imcomplete data via EM algorithm. Journal of the Royal Statistical Society (Series B), 39: 1-38, 1977.
68. Dentener PR, Whiting DC, Connolly PG. Thrips palmi Karny (Thysanoptera: Thripidae): Could it survive in New Zealand? New Zealand Plant Protection, 55: 18-22, 2002.
69. Department of Mathematics of Nanjing University. Ordinary Differential Equations. Science Press, Beijing, China, 1978.
70. Dimopoulos I, Chronopoulos J, Chronopoulou-Sereli A, Lek S. Neural network models to study relationships between lead concentration in grasses and permanent urban descriptors in Athens city (Greece). Ecological Modelling, 120: 157-165, 1999.
71. Dong BL, Ji LZ, Wei CY et al. Relationship between plant community and insect community in Korean pine broad-leaved mixed forest of Changbai Mountain. Chinese Journal of Ecology, 24(9): 1013-1016, 2005.
72. Dony JG. The expectation of plant records from prescribed areas. Watsonia, 5: 377-385, 1963.
73. Efron B, Gong G.A leisurely look at the bootstrap, the jackknife, and cross-validation. The American Statistician, 37: 36-48, 1983.
74. Eyre MD, Rushton SP, Luff ML, Telfer MG. Investigating the relationships between the distribution of British ground beetle species (Coleoptera, Carabidae) and temperature, precipitation and altitude. Journal of Biogeography, 32: 973-983, 2005.
75. Fecit. Analysis and Design of Neural Networks in MATLAB 6.5. Electronics Industry Press, Beijing, China, 2003.
76. Filippi AM, Jensen JR. Fuzzy learning vector quantization for hyperspectral coastal vegetation classification. Remote Sensing of Environment, 100: 512-530, 2006.
77. Flexer A. Statistical evaluation of neural network experiments: Minimum requirements and current practice. Cybernetics and Systems’96 Proceedings of the 13th European Meeting on Cybernetics and Systems Research, Trappl R (Ed.). 1005-1008, Austrian Society for Cybernetic Studies, 1996.
78. Frean M. The upstart algorithm:A method for constructing and training feed forward neural networks. Neural Computation, 2(2): 198-209, 1990.
79. Gao XR, Yang FS. A new method for training MLP neural networks. Chinese Journal of Computers, 19(6): 687-694, 1996.
80. Gardner MW, Dorling SR. Artificial neural networks (the multiplayer perceptron)—areview of applications in the atmospheric sciences. Atmospheric Environment, 32: 2627-2636, 1998.
81. Gardner MW, Dorling SR. Neural network modelling and prediction of hourly NOx and NO2 concentrations in urban air in London. Atmospheric Environment, 33(5): 709-719, 1999.
82. Gardner MW, Dorling SR. Statistical surface ozone models: An improved methodology to account for non-linear behaviour. Atmospheric Environment, 34(1): 21-34, 2000.
83. Gentle JE. Elements of Computational Statistics. Springer Science+Business Media, Inc., Netherlands, 2002.
84. Gevrey M, Dimopoulos I, Lek S. Review and comparison of methods to study the contribution of variables in artificial neural network models. Ecological Modelling, 160: 249-264, 2003.
85. Gevrey M, Dimopoulos I, Lek S. Two-way interaction of input variables in the sensitivity analysis of neural network models. Ecological Modelling, 195: 43-50, 2006.
86. Gotelli NJ, Graves GR. Null Models in Ecology. Smithsonian Institution Press, Washington DC, USA, 1996.
87. Grime JP. Benefits of plant diversity to ecosystems: Immediate, filter and founder effects. Journal of Ecology, 86: 902-910, 1998.
88. Grossberg S. Adaptive pattern classification and universal recoding: I. Parallel development and coding of neural feature detectors. Biological Cybernetics, 23: 121-134, 1976.
89. Grossberg S. Howdoes the brain build a cognitive code? Psychological Review, 88: 375-407, 1980.
90. Gu HZ, Takahashi H. Towards more practical average bounds on supervised learning. IEEE Transactions on Neural Networks, 7(44): 953-968, 1996.
91. Guisan A, Edwards TC, Hastie T. Generalized linear and generalized additive models in studies of species distributions: Setting the scene. Ecological Modelling, 157: 89-100, 2002.
92. Hagan MT, Demuth HB, Beale MH. Neural Network Design. PWS Publishing Company, Boston, USA, 1996.
93. Hagan MT, Menhaj M. Training feedforward networks with the Marquardt algorithm. IEEE Transactions on Neural Networks, 5(6): 989-993, 1994.
94. Haykin S. Neural Networks: A Comprehensive Foundation. Macmillan College Publishing Company, NewYork, USA, 1994.
95. He RB. MATLAB 6: Engineering Computation and Applications. Chongqing University Press, Chongqing, 2001.
96. Hebb DO. The Organization of Behavior.Wiley, NewYork, USA, 1949.
97. Hellmann JJ, Fowler GW. Bias, precision, and accuracy of foue measures of species richness. Ecological Applications, 3: 824-834, 1999.
98. Heltshe JF, Forester NE. Estimating species richness using the jackknife procedure. Biometrics, 39: 1-11, 1983.
99. Herrick JE, Bestelmeyer BT, Archer S, Tugel AJ, Brown JR. An integrated framework for science-based arid land management. Journal of Arid Environments, 65: 319-335, 2006.
100. Hinton GE, Sejnowski TJ, Ackley DH. Boltzmann Machines: Constraint Satisfaction Networks that Learn. Carnegie-Mellon University Computer Science Technical Report: CMU-CS-84-119, Carnegie-Mellon University, Pittsburgh, USA, 1984.
101. Hochberg MM et al.HMM/NN training techniques for connected alphadigit speech recognition. Proc. IEEE ICASSP’91, Toronto, Canada, 109-112, 1991.
102. Hoffmann A. Paradigms of Artificial Intelligence: A methodological and Computational Analysis. Springer, Singapore, 1998.
103. Hopfield JJ. Neural networks and physical systems with emergent collective computational abilities. Proceedings of the National Academy of Sciences USA, 79: 2554-2558, 1982.
104. Hopfield JJ. Neurons with graded response have collective computational properties like those of two-state neurons. Proceedings of the National Academy of Sciences USA, 81: 3088-3092, 1984.
105. Hopfield JJ, Tank DW. Neural computation of decisions in optimization problems. Biological Cybernetics, 52: 141-154, 1985.
106. Hornik KM, Stinchcombe M, White H. Multilayer feedforward networks are universal approximators. Neural Networks, 2(5): 359-366, 1989.
107. Hsu KJ. Time series analysis of the interdependences among air pollutant. Atmospheric Environment, 26(4): 491-503, 1992.
108. Hurt NE. Phase Retrieval and Zero Crossing. Kluwer Academic Publisher, NewYork, USA, 1989.
109. Hurlbert SH. The concept of species diversity: A critique and alternative parameters. Ecology, 52: 577-585, 1971.
110. Ingber L, Rosen B. Generic algorithms and very fast simulated reannealing:A comparison. Ecology, 16: 87-100.
111. Ingber L. Simulated annealing: Practice versus theory. Mathematical Computer and Modelling, 18: 29-57, 1993.
112. Jaarsma NG, Bergman M, Schulze FH, deviate AB. Macro-invertebrates in a dynamic river environment: Analysis of time series from artificial substrates, using a ‘white box’ neural network modelling method. Aquatic Ecology, 41(3): 413-425, 2007.
113. Jacobs RA. Increased rates of convergence through learning rate adaptation. Neural Networks, 1(4): 295-308, 1988.
114. Jacobs RA, Jordan MI, Nowlan SJ, Hinton GE. Adaptive mixtures of local experts. Neural Computation, 3: 79-87, 1991.
115. Jackson RD, Bartolome JW. A state-transition approach to understanding nonequilibrium plant community dynamics in Californian grasslands. Plant Ecology, 162: 49-65, 2002.
116. Jasinski JP, Payette S. The creation of alternative stable states in the sourthern boreal forest, Québec, Canada. Ecological Monographs, 75: 561-583, 2005.
117. Jia CS, Chi DF, Hu YY. Effects of forest plant communities on forest insect communities. Journal of Anhui Agricultural Science, 34(9): 1871-1872, 2006.
118. Jordan MI, Jacobs RA. Hierarchies of adaptive experts. Advances in Neural Information Processing Systems4. San Mateo, USA, 1992.
119. Jorgensen SE, Verdonschot P, Lek S. Explanation of the observed structure of functional feeding groups of aquatic macro-invertebrates by an ecological model and the maximum exergy principle. Ecological Modelling, 158: 223-231, 2002.
120. Kaluli JW, Madramootoo CA, DjebbarY. Modeling nitrate leaching using neural networks. Water Science and Technology, 38(7): 127-134, 1998.
121. Kemp SJ, Zaradic P, Hansen F.An approach for determining relative input parameter importance and significance in artificial neural networks. Ecological Modelling, 204: 326-334, 2007.
122. Kilic H, Soyupak S, Tuzun I et al.An automata networks based preprocessing technique for artificial neural network modelling of primary production levels in reservoirs. Ecological Modelling, 201: 359-368, 2007.
123. Kohonen T. Correlation matrix memories. IEEE Transactions on Computers, 21: 353-359, 1972.
124. Kohonen T. Self-organizing formation of topologically correct feature maps. Biological Cybernatics, 43: 59-69, 1982.
125. Kohonen T. Self-Organization and Associative Memory(3rd Edition). Springer-Verlag, New York, USA, 1988.
126. Kohonen T. The self-organizing map. Proceedings of the IEEE, 78(9): 1464-1480, 1990.
127. Kohonen T. Self-Organizing Maps. Springer-Verlag, Heidelberg, Germany, 1995.
128. Kohonen T, Somervuo P. Self-organizing maps of symbol strings. Neurocomputing, 21: 19-30, 1998.
129. Krebs CJ. Ecological Methodology. HarperCollinsPublishers, Inc., USA, 1989.
130. Kremen C, Colwell RK, Erwin TL, Murphy DD. Invertebrate assemblges: Their use as indicators in conservation planning. Conservation Biology, 7: 796-808, 1993.
131. Kuo JT, Hsieh MH, Lung WS et al.Using artificial neural network for reservoir eutrophication prediction. Ecological Modelling, 200: 171-177, 2007.
132. Laird N. The EM algorithm. Handbook of Statistics Vol 9, Rao CR (Ed.). 509-520, Elsevier Science Publisher, The Netherlands, 1993.
133. Lars OB. Stratospheric ozone, ultraviolet radiation, and cryptogams. Biological Conservation, 135(3): 326-333, 2007.
134. Lavorel S, Garnier E. Predicting the effects of environmental change on plant community composition and ecosystem functioning: Revising the Holy Grail. Functional Ecology, 16: 545-556, 2002.
135. LeCun Y. Une procedure d’apprentissage pour reseau a seuil assymetrique. Cognitiva, 85: 599-604, 1985.
136. Lee WT, Tenorio MF. On an asymptotically optimal adaptive classifier design criterion. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15(3): 312-318, 1991.
137. Lehmann A, Overton J, Leathwick JJ. GRASP: Generalized regression analysis and spatial prediction. Ecological Modelling, 157: 189-207, 2002.
138. Lek S, Baran P. Estimations of trout density and biomass: A neural networks approach. Nonlinear Analysis, Theory, Methods & Applications, 30(8): 4985-4990, 1997.
139. Lek S, Belaud A, Baran P, Dimopoulos I, Delacoste M. Role of some environmental variables in trout abundance models using neural networks. Aquatic Living Resources, 9: 23-29, 1996.
140. Li QY, Wang NC, Yi DY. Numerical Analysis(Fourth Edition). Tsinghua University Press, Springer, Beijing, China, 2001.
141. Liang Y, Page EW. Multiresolution learning paradigm and signal prediction. IEEE Transactions on Signal processing, 45: 2858-2864, 1997.
142. Lin JK. Foundations of Topology. Science Press, Beijing, China, 1998.
143. Liu BC. Functional Analysis. Science Press, Beijing, China, 2000.
144. Loot G, Giraudel JL, Lek S. A non-destructive morphometric technique to predict Ligula intestinalis L. plerocercoid load in roach (Rutilus rutilus L.) abdominal cavity. Ecological Modelling, 156: 1-11, 2002.
145. Luo SW. Theoretical Principles of Large-scale Artificial Neural Networks. Tsinghua University Press, Northern Jiaotong University Press, 2004.
146. Mackey DJC. Bayesian interpolation. Neural Computation, 4: 415-447, 1992.
147. Maier HR, Dandy GC. Understanding the behaviour and optimising the performance of back-propagation neural networks:An empirical study. Environmental Modelling & Software, 13(2): 179-191, 1998.
148. Manly BFJ. Randomization, Bootstrap and Monte Carlo Methods in Biology(2nd Edition). Chapman & Hall, London, Britain, 1997.
149. Maravelias CD, Haralabous J, Papaconstantinou C. Predicting demersal fish species distributions in the Mediterranean Sea using artificial neural networks. Marine Ecology-Progress Series, 255: 249-258, 2003.
150. Marchand M, Golea M, Ruján P. A convergence theorem for sequential learning in twolayer perceptrons. Europhysics Letters, 11(65): 487-492, 1990.
151. Marchant JA, Onyango CM. Comparison of a Bayesian classifier with a multilayer feedforward neural network using the example of plant/weed/soil discrimination. Computers and Electronics in Agriculture, 39: 3-22, 2003.
152. Mathworks. MATLAB 6.5: Neural Network Toolbox. Mathworks, Natick, USA, 2002.
153. McCulloch W, Pitts W. A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics, 5: 115-133, 1943.
154. McKay et al. A comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics, 21: 239-245, 1979.
155. McKenna JE. Application of neural networks to prediction of fish diversity and salmonid production in the Lake Ontario basin. Transactions of The American Fisheries Society, 134(1): 28-43, 2005.
156. Men SP, Feng JH. Applied Functional Analysis. Science Press, Beijing, China, 2005.
157. Meng DJ, Liang K. Differential Geometry. Science Press, Beijing, China, 1999.
158. Mezard M, Nadal JP. Learning in feedforward layered network: The tiling algorithm. Journal of Physics A, 22: 2191-2204, 1989.
159. Miller RJ, Wiegert RG. Documenting completeness, species-area relations, and the speciesabundance distribution of a regional flora. Ecology, 70: 16-22, 1989.
160. Miller RJ, White PS. Condiderations for preserve design based on the distribution of rare plants in Great Smoky Mountains National Park. U.S.A. Journal of Environmental Management, 10: 119-124, 1986.
161. Minsky M, Papert S. Perceptrons. MIT Press, Cambridge, USA, 1969.
162. Moisen GG, Frescino TS. Comparing five modelling techniques for predicting forest characteristics. Ecological Modelling, 157: 209-225, 2002.
163. Moller MF. A scaled conjugate gradient algorithm for fast supervised learning. Neural Networks, 6: 525-533, 1993.
164. Moody JE, Darken CJ. Learning with a localized receptive fields. Proceedings of the 1988 Connectionist Model Summer School. Morgan Kaufmann Publishers, San Mateo, USA, 133-143, 1988.
165. Moody JE, Darken CJ. Fast learning in networks of locally-tuned processing units. Neural Computation, 1: 281-294, 1989.
166. Moreau S, Bosseno R, Gu X., Baret F. Assessing the biomass dynamics of Andean bofedal and totora high-protein wetland grasses from NOAA/AVHRR. Remote Sensing of Environment, 85: 516-529, 2003.
167. Moreno CE, Halffter G. Assessing the completeness of bat biodiversity inventories using species accumulation curves. Journal of Applied Ecology, 37: 149-158, 2000.
168. Nagendra SMS, Khare M. Artificial neural network approach for modelling nitrogen dioxide dispersion from vehicular exhaust emissions. Ecological Modelling, 190: 99-115, 2006.
169. Narendra KS, Mukhopadhyay S. Adaptive control of nonlinear multivariable systems using neural networks. IEEE Transactions on Neural Networks, 8(3): 475-485, 1997.
170. Narendra KS, Mukhopadhyay S. Adaptive control of nonlinear multivariable systems using neural networks. Neural Networks, 7(5): 737-752, 1994.
171. NeuralWare. Neural Computing. NeuralWare Inc., Pittsburgh, USA, 2000.
172. Nour MH, Smith DW, El-Din MG et al.The application of artificial neural networks to flow and phosphorus dynamics in small streams on the Boreal Plain, with emphasis on the role of wetlands. Ecological Modelling, 191: 19-32, 2006.
173. Olden JD. An artificial neural network approach for studying phytoplankton succession. Hydrobiology, 436: 131-143, 2000.
174. Olden JD, Jackson DA. Illuminating the “black box”:A randomization approach for understanding variable contributions in artificial neural networks. Ecological Modelling, 154: 135-150, 2002.
175. Olden JD, Joy MK, Death RG. Rediscovering the species in community-wide predictive modeling. Ecological Applications, 16(4): 1449-1460, 2006.
176. Olden JD, Joy MK, Death RG. An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data. Ecological Modelling, 178: 389-397, 2004.
177. Özesmi SL, Özesmi U. An artificial neural network approach to spatial habitat modeling with interspecific interaction. Ecological Modelling, 116: 15-31, 1999.
178. Özesmi SL, Tan CO, Özesmi U. Methodological issues in building, training, and testing artificial neural networks in ecological applications. Ecological Modelling, 195: 83-93, 2006.
179. Pal NR, Bezdek JC, Tsao ECK. Generalized clustering networks and Kohonen’s selforganizing scheme. IEEE Transcations on Neural Networks, 4: 549-557, 1993.
180. Palmer MW. The estimation of species richness by extrapolation. Ecology, 71: 1195-1198, 1990.
181. Palmer MW. Estimating species richness: The second-order jackknife reconsidered. Ecology, 72: 1512-1513, 1991.
182. Pao YH et al. Neural-net computing and intelligent control systems. International Journal of Control, 56: 263-289, 1992.
183. Park J et al.Universal approximations using RBF networks. Neural Computation, 3: 246-257, 1991.
184. Park YS, Chang JB, Lek S, Cao WX, Brosse S. Conservation strategies for endemic fish species threatened by the Three Gorges Dam. Conservation Biology, 17: 1748-1758, 2003.
185. Parker DB. Learning-logic: Casting the cortex of the human brain in silicon.Technical Report TR-47, Center for Computational Research in Economics and Management Science, MIT, Cambridge, USA, 1985.
186. Paruelo JM, Tomasel F. Prediction of functional characteristics of ecosystems:Acomparison of artificial neural networks and regression models. Ecological Modelling, 98: 173-186, 1997.
187. Paschalidou AK, Iliadis LS, Kassomenos P, Bezirtzoglou C. Neural modelling of the tropospheric ozone concentrations in an urban site. Proceedings of the 10th International Conference on Engineering Applications of Neural Networks. Thessaloniki, Hellas, 29-31 Aug, 2007.
188. Pastor-Barcenas O, Soria-Olivas E, Martın-Guerrero JD. Unbiased sensitivity analysis and pruning techniques in neural networks for surface ozone modeling. Ecological Modelling, 182: 149-158, 2005.
189. Pearson RG, Dawson TP, Berry PM et al.SPECIES:A spatial evaluation of climate impact on the envelope of species. Ecological Modelling, 154(3): 289-300, 2002.
190. Peacock L, Worner SP, Sedcole R. Climate variables and their role in site discrimination of invasive insect species distributions. Environmental Entomology, 35(4): 958-963, 2006.
191. Perterson C, Anderson JR. Amean field theory learning algorithm for NN. Complex Systems, 1(5): 995-1019, 1987.
192. Pidgeon IM, Ashby E. Studies in applied ecology I: A statistical analysis of regeneration following protection from grazing. Proceedings of the Linneon Society New SouthWales, 65: 123-143, 1940.
193. Pimental D, Stachow U, Takacs DA, Brubaker HW. Conserving biological diversity in agricultural/forestry systems. Bioscience, 42(5): 354-362, 1992.
194. Poggio T, Girosi F. Networks for approximation and learning. Proceedings of IEEE, 78(9): 1481-1496, 1990.
195. Powel MJD. The theory of radial basis function approximation. University of Cambridge Numerical Analysis Reports. University of Cambridge, London, UK, 1990.
196. Prechelt L. A quantitative study of experimental evaluations of neural network learning algorithms: Current research practice. Neural Networks, 9(3): 457-462, 1996.
197. Preston FW. Time and space and the variation of species. Ecology, 41: 785-790, 1960.
198. Pu ZL. Mathematical Models and Applications in the Management of Crop Insect Pests. Guangdong Science and Technology Publishing House, Guangzhou, China, 1990.
199. Quétier F, Thébault A, Lavorel S. Plant traits in a state and transition framework as markers of ecosystem response to land-use change. Ecological Monographs, 77(1): 33-52, 2007.
200. Rabiner LR. A tutorial on HMM and selected applications in speech recognition. Proc. IEEE, 77: 257-286, 1989.
201. Racca JMJ, Racca R, Pienitz R, Prairie YT. PaleoNet: New software for building, evaluating and applying neural network based transfer functions in paleoecology. Journal of Paleolimnology, 38(3): 467-472, 2007.
202. Ray C, Klindworth K. Neural networks for agrichemical vulnerability assessment of rural private wells. Journal of Hydrologic Engineering, 5(2): 162-171, 2000.
203. Recknagel F, French M, Harkonen P, Yabunaka K. Artificial neural network approach for modelling and prediction of algal blooms. Ecological Modelling, 96: 11-28, 1997.
204. Reed R. Prunning algorithm-a survey. IEEE Transactions on Neural Networks, 4(5): 740-747, 1993.
205. Reyjol Y, Lim P, Belaud A, Lek S. Modelling of microhabitat used by fish in natural and regulated flows in the river Garonne (France). Ecological Modelling, 146: 131-142, 2001.
206. Rosenblatt F. The perceptron:Aprobabilistic model for information storage and organization in the brain. Psychological Review, 65: 388-408, 1958.
207. Rosenzweig ML. Species Diversity in Space and Time. Cambridge University Press, Cambridge, USA, 1995.
208. Rossi RE, Borth PW, Tollefson JJ. Stochastic simulation for characterizing ecological spatial patterns and appraising risk. Ecological Applications, 3(4): 719-735, 1993.
209. Rozema J, Boelen P, Blokker P. Depletion of stratospheric ozone over the Antarctic and Arctic: Responses of plants of polar terrestrial ecosystems to enhanced UV-B. Environmental Pollution, 137(3): 428-442, 2005.
210. Rudin W. Functional Analysis (Second Edition). McGraw-Hill, Columbus, USA, 1991.
211. Rumelhart DE et al.Learning representation by BP errors. Nature, 7: 149-154, 1986.
212. Rumelhart DE, Hinton GE, Williams RJ. Learning representations by back-propagation errors. Nature, 323: 533-536, 1986.
213. Rumelhart DE, McClelland JL. Parallel Distributed Processing: Explorations in the Microstructure of Cognition Vol. 1. MIT Press, Cambridge, USA, 1986.
214. Sanders HL. Marine benthic diversity:A comparative study. The American Naturalist, 102: 243-282, 1968.
215. Sarle WS. Stopped training and other remedies for overfitting. Proceedings of the 27th Symposium on the Interface of Computing Science and Statistics, 352-360, 1995.
216. Scardi M. Artificial neural networks as empirical models for estimating phytoplankton production. Marine Ecology Progress Series, 139: 289-299, 1996.
217. Scardi M, Harding LW. Developing an empirical model of phytoplankton primary production: A neural network case study. Ecological. Modelling, 120: 213-223, 1999.
218. Scheffe RD, Morris RE. A review of the development and application of the urban airshed model. Atmospheric Environment, 27b(1): 23-39, 1993.
219. Schino G, Iannetta M, Martini S et al.Satellite estimate of grass biomass in a mountainous range in central Italy. Agroforestry Systems, 59: 157-162, 2003.
220. Schoenly KG, Cohen MB, Barrion AT, Zhang WJ, Gaolach B, Viajante VD. Effects of Bacillus thuringiensis on non-target herbivore and natural enemy assemblages in tropical irrigated rice. Environment and Biosafety Research, 3: 181-206, 2003.
221. Schoenly KG, Zhang WJ. IRRI Biodiversity Software Series. I. LUMP, LINK, AND JOIN: Utility programs for biodiversity research. IRRI Technical Bulletin No. 1. Manila, Philippines, International Rice Research Institute, 1999.
222. Schoenly KG, Zhang WJ. IRRI Biodiversity Software Series. V. RARE, SPPDISS, and SPPANK: programs for detecting between-sample difference in community structure. IRRI Technical Bulletin No. 5. International Rice Research Institute, Manila, Philippines, 1999.
223. Schultz A, Wieland R. The use of neural networks in agroecological modeling. Computers and Electronics in Agriculture, 18: 73-90, 1997.
224. Scott I, Mulgrew B. Nonlinear system identification and prediction using orthonormal functions. IEEE Transactions on Signal Processing, 45: 1842-1853, 1997.
225. Setiono R.A penalty-function approach for pruning feedforward NN. Neural Computation, 9(1): 185-204, 1997.
226. Shahid SA, Schoenly KG, Haskell NH, Hall RD, Zhang WJ. Carcass enrichment does not alter decay rates or arthropod community structure: A test of the arthropod saturation hypothesis at the anthropology research facility in Knoxville, Tennessee. Journal of Medical Entomology, 4: 559-569, 2003.
227. Shang Y, Wah BW. Global optimization for NN training. IEEE Transactions on Computer, 29(3): 45-54, 1996.
228. Shanmuganathan S, Collins P, Buckeridge J. Self-organising map methods in integrated modelling of environmental and economic systems. Environment Modelling and Software, 21(9): 1247-1256, 2006.
229. Shanno DF. Recent advances in numerical techniques for large-scale optimization. Neural Networks for Control, Miller, Sutton, and Werbos (Eds.). MIT Press, Cambridge, USA, 1990.
230. Sharma V, Negi SC, Rudra RP et al.Neural networks for predicting nitrate-nitrogen in drainage water. Agricultural Water Management, 63: 169-183, 2003.
231. Sheng SG, Gao BJ, Zhang YH, Wang ZW, Wang HX, Wang XJ. Studies on the structure of the insect communities in different plant types. Journal of Agricultural University of Hebei, 20(4): 61-65, 1997.
232. Shi MG, Gu LZ. Foundations of Scientific and Engineering Computation. Tsinghua University, Beijing, China, 1999.
233. Simberloff D. Properties of the rarefaction diversity measurements. The American Naturalist, 196: 414-418, 1972.
234. Simpson RW, Layton AP. Forecasting peak ozone levels. Atmospheric Environment, 17: 1649-1654, 1983.
235. Smith EP, van Belle G. Non-parametric estimation of species richness. Biometrics, 40: 119-129, 1984.
236. Smith M. Neural Networks for Statistical Modeling. Van Nostrand Reinhold, New York, USA, 1994.
237. Soltic S, Pang S, Peacock L, Worner SP. Evolving computation offers potential for estimation of pest establishment. International Journal of Computers, Systems and Signals, 5(2): 36-43, 2004.
238. Song MY, Hwang HJ, Kwark IS et al.Self-organizing mapping of benthic macroinvertebrate communities implemented to community assessment and water quality evaluation. Ecological Modelling, 203: 18-25, 2007.
239. Sontag ED. VC dimension of neural networks. Neural Networks and Machine Learning, Bishop CM (Ed.). 69-95, NATO ASI Series, Springer, 1998.
240. Sprecht DF. Probabilistic neural networks for classification, mapping and associative memory. IEEE ICNN. San Diego, USA, 1988.
241. Sprecht DF. Probabilistic neural networks. Neural Networks, 3(1): 109-118, 1990.
242. Sprecht DF. A general regression neural network. IEEE Transcations on Neural Networks, 2(6): 568-576, 1991.
243. Steele BB, Bayn RL, ValGrant C. Environmental monitoring using populations of birds and small mammals: Analysis of sampling effort. Biological Conservation, 30: 157-172, 1984.
244. Sutherst RW, Maywald GA. Climate model of the red imported fire ant, Solenopsis invicta Buren (Hymenoptera: Formicidae): Implications for invasion of new regions, particularly Oceania. Environmental Entomology, 34(2): 317-335, 2005.
245. Swingler K. Applying Neural Networks: A Practical Guide. Academic Press, London, UK, 1996.
246. Tan CO, Ozesmi U, Beklioglu M et al.Predictive models in ecology: Comparison of performances and assessment of applicability. Ecological Informatics, 1(2): 195-211, 2006.
247. Tison J, Park YS, Coste M, Wasson JG, Rimet F, Ector L, Delmas F. Predicting diatom reference communities at the French hydrosystem scale:Afirst step towards the definition of the good ecological status. Ecological Modelling, 203: 99-108, 2007.
248. Tollenaere T. SuperSAB: Fast adaptation back propagation with good scaling properties. Neural Networks, 3(5): 561-573, 1990.
249. Valiant LG. A theory of the learnable. Communications of the ACM, 27(11): 1134-1142, 1984.
250. Vahidinasab V. Day-ahead price forecasting in restructured power systems using artificial neural networks. Electric Power Systems Research, 78(8): 1332-1342, 2008.
251. vanderWerf W, Keesman K, Burgesss P et al.Yield-SAFE: A parameter- sparse, processbased dynamic model for predicting resource capture, growth, and production in agroforestry systems. Ecological Engineering, 29(4): 419-433, 2007.
252. Van Rooij AJF, Jain LC, Johnson RP. Neural Netowork Training Using Generic Algorithm. World Scientific, Singapore, 1996.
253. Vapnik V. The Nature of Statistical Learning Theory. Springer-Verlag, New York, USA, 1995.
254. Vapnik V. An overviewof statistical learning theory. IEEE Transactions on Neural Networks, 10: 988-999, 1999.
255. Venkatech S. Computation and learning in the context of neural network capacity. Neural Networks for Perception Vol 2, Wechsler H (Ed.). 173-207, Academic Press, 1992.
256. Viotti P, Liuti G, Di Genova P. Atmospheric urban pollution: Applications of an artificial neural network (ANN) to the city of Perugia. Ecological Modelling, 148(1): 27-46, 2002.
257. Vogl TP, mangis JK, Zigler AK, Zink WT, Alkon DL. Accelerating the convergence of the backpropagation method. Biological Cybernetics, 59: 256-264, 1988.
258. Vonder Malsburg. Network self-organization.An Introduction to Neural and Eletric NetworkZornetger SF et al. (eds.). Academic Press, USA, 1990.
259. Wagner TL, Wu H, Sharpe PJH et al.Modeling distribution of insect development time: A literature review and application of the Weibull function. Annuals of Entomological Society of America, 77: 475-487, 1984.
260. Walther BA, Morand S. Comparative performance of species richness estimation methods. Parasitology, 116: 395-405, 1998.
261. Wasserman PD. Neural Computing: Theory and Practice. Van Nostrand Reinhold, New York, USA, 1989.
262. Watts MJ, Worner SP. Using artificial neural networks to determine the relative contribution of abiotic factors influencing the establishment of insect pest species. Ecological Informatics, 3: 64-74, 2008.
263. Watts MJ, Worner SP. Estimating the risk of insect species invasion:Kohonen self-organising maps versus k-means clustering. Ecological Modelling, 220: 821-829, 2009.
264. Way MJ, Heong KL. The role of biodiversity in the dynamics and management of insect pests of tropical irrigated rice-a review. Bulletin of Entomological Research, 84: 567-587, 1994.
265. Werboss PJ. Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Science. PhD Thesis, Harvard University, Cambridge, USA, 1974.
266. Wettschereck D et al.Improving the performance of RBF network by learning center locations. Advances in Neural Information Processing Systems. San Mateo, USA, 1992.
267. Widrow B. Neural networks application in industry, business and science. Communication of the ACM, 37: 93-105, 1994.
268. Widrow B, Hoff ME. Adaptive switching circuits. 1960 IRE WESCON Convention Record, IRE Part 4, NewYork, USA, 1960.
269. Widrow B, Stearns SD. Adaptive Signal Processing. Prentice-Hall, New Jersy, USA, 1985.
270. Widrow B, Winter R. Neural nets for adaptive filtering and adaptive pattern recognition. IEEE Computer Magazine, 3: 25-39, 1988.
271. Williams CB. Patterns in the Balance of Nature. Academic Press, London, England, 1964.
272. Willmott CJ. Some comments on the evaluation of model performance. Bulletin of the American Meteorological Society, 63: 1309-1313, 1982.
273. Wilson EO. The little things that run the world. Conservation Biology, 1: 344-346, 1987.
274. Worner SP. Predicting the establishment of exotic pests in relation to climate. Quarantine Treatments for Pests of Food Plants, Sharp JL, Hallman GJ (Eds.).Westview Press, Boulder, USA, 1994.
275. Worner SP, Gevrey M. Modelling global insect pest species assemblages to determine risk of invasion. Journal of Applied Ecology, 43(5): 858-867, 2006.
276. Wu DR. Course Notes on Differential Geometry. Higher Education Press, Beijing, China, 1981.
277. Xie YC, Sha ZY, Yu M et al. A comparison of two models with Landsat data for estimating above ground grassland biomass in Inner Mongolia, China. Ecological Modelling, 220: 1810-1818, 2009.
278. Xu L. Bayesian ying-yang system and theory as a unified statistical learning approach (1): For unsupervised and semi-unsupervised learning. Brain-Like Computing and Intelligent Information Systems, Amari S, Kasabov N (Eds.). Springer-Verlag, Germany, 241-274, 1997.
279. Yan PF, Zhang CS. Artificial Neural Networks and Computation of Simulated Evolution. Tsinghua University Press, Beijing, China, 2000.
280. Yazdanpanah H.A Neural network model to predict wheat yield. Proceedings of Map India Conference, New Delhi, India, 2002.
281. Yazdanpanah H, Karimi M, Hejazizadeh Z. Forecasting of daily total atmospheric ozone in Isfahan. Environmental Monitoring and Assessment(DOI 10.1007/s10661-008-0531-z), 2008.
282. Yu R, Leung PS, Bienfang P. Predicting shrimp growth: Artificial neural network versus nonlinear regression models. Aquacultural Engineering, 34: 26-32, 2006.
283. Zanetti P. Air Pollution Modelling: Theories, Computational Methods and Available Software. omputational Mechanics Publications, Southampton, Boston, USA, 1990.
284. Zhang WJ. Computer inference of network of ecological interactions from sampling data. Environmental Monitoring and Assessment, 124: 253-261, 2007a.
285. Zhang WJ. Methodology on Ecology Research. SunYat-Sen University Press, Guangzhou, China, 2007.
286. Zhang WJ. Supervised neural network recognition of habitat zones of rice invertebrates. Stochastic Environmental Research and Risk Assessment, 21: 729-735, 2007c.
287. Zhang WJ. Pattern classification and recognition of invertebrate functional groups using selforganizing neural networks. Environmental Monitoring and Assessment, 130: 415-422, 2007d.
288. Zhang WJ, Bai CJ, Liu GD. Neural network modeling of ecosystems: A case study on cabbage growth system. Ecological Modelling, 201: 317-325, 2007.
289. Zhang WJ, BarrionAT. Function approximation and documentation of sampling data using artificial neural networks. Environmental Monitoring and Assessment, 122: 185-201, 2006.
290. Zhang WJ, Feng YJ, Schoenly KG. Performance of non-parametric richness estimators to hierarchical invertebrate taxa in irrigated rice field. International Rice Research Notes, 29(1): 39-41, 2004.
291. Zhang WJ, Li QH. Development of topological functions in neural networks and their application in SOM learning to biodiversity. The Proceedings of the China Association for Science and Technology, 4(2): 583-586, 2007.
292. Zhang WJ, Liu GH, Dai HQ. Simulation of food intake dynamics of holometabolous insect using functional link artificial neural network. Stochastic Environmental Research and Risk Assessment, 22: 123-133, 2008.
293. Zhang WJ, Pang Y, Qi YH et al.Relationship between temperature and development of Spodoptera litura F. ACTA SCIENTIARUM NATURALIUM UNIVERSITATIS SUN YATSENI, 36(2): 6-9, 1997.
294. Zhang WJ, Qi YH. Functional link artificial neural network and agri-biodiversity analysis. Biodiversity Science, 3: 345-350, 2002.
295. Zhang WJ, Schoenly KG. IRRI Biodiversity Software Series. II. COLLECT1 and COLLECT2: Programs for calculating statistics of collectors’ curves. IRRI Technical Bulletin No. 2. International Rice Research Institute, Manila, Philippines, 1999.
296. Zhang WJ, Schoenly KG. IRRI Biodiversity Software Series. IV. EXTSPP1 and EXTSPP2: Programs for comparing and performance-testing eight extrapolation-based estimators of total species richness. IRRI Technical Bulletin No. 4. International Rice Research Institute, Manila, Philippines, 1999.
297. Zhang WJ, Schoenly KG. Lumping and correlation analyses of invertebrate taxa in tropical irrigated rice field. International Rice Research Notes, 1: 41-43, 2004.
298. Zhang WJ, Wei W. Spatial succession modeling of biological communities: A multi-model approach. Environmental Monitoring and Assessment, 158: 213-230, 2009.
299. Zhang WJ, Zhang XY. Neural network modeling of survival dynamics of holometabolous insects: A case study. Ecological Modelling, 211: 433-443, 2008.
300. Zhang WJ, Zhong XQ, Liu GH. Recognizing spatial distribution patterns of grassland insects: Neural network approaches. Stochastic Environmental Research and Risk Assessment, 22(2): 207-216, 2008.
301. Zhang YT, Fang KT. Introduction to Multivariable Statistical Analysis. Science Press, Beijing, China, 1982.