-
1
-
-
53249119799
-
Classification with artificial neural networks and support vector machines: application to oil fluorescence spectra
-
Almhdi KM, Valigi P, Gulbinas V, Westphal R, Reuter R (2007) Classification with artificial neural networks and support vector machines: application to oil fluorescence spectra. Eur Assoc Remote Sens Lab (EARSeL) eProceeding 6(2):115–129
-
(2007)
Eur Assoc Remote Sens Lab (EARSeL) eProceeding
, vol.6
, Issue.2
, pp. 115-129
-
-
Almhdi, K.M.1
Valigi, P.2
Gulbinas, V.3
Westphal, R.4
Reuter, R.5
-
2
-
-
58449121075
-
Optimum learning rate in back-propagation neural network for classification of satellite images (IRS-1D)
-
Amini J (2008) Optimum learning rate in back-propagation neural network for classification of satellite images (IRS-1D). Sci Iran 15(6):558–567
-
(2008)
Sci Iran
, vol.15
, Issue.6
, pp. 558-567
-
-
Amini, J.1
-
4
-
-
84907691010
-
-
Accessed 2 Apr 2014
-
Borne KD (2005) UMUC data mining lecture 2. http://polaris.umuc.edu/its/CSMN/csmn667/lecture02.ppt. Accessed 2 Apr 2014
-
(2005)
UMUC data mining lecture 2
-
-
Borne, K.D.1
-
5
-
-
0005380346
-
Neural networks primer, part III
-
Caudill M (1988) Neural networks primer, part III. AI Expert 3(6):53–59
-
(1988)
AI Expert
, vol.3
, Issue.6
, pp. 53-59
-
-
Caudill, M.1
-
6
-
-
67650677066
-
Combining back-propagation and genetic algorithms to train neural networks for ambient temperature modeling in Italy
-
Springer, Berlin:
-
Ceravolo F, Felice MD, Pizzuti S (2009) Combining back-propagation and genetic algorithms to train neural networks for ambient temperature modeling in Italy. In: Giacobini et al. (ed) Applications of evolutionary computing. Springer, Berlin, pp 123–131
-
(2009)
Applications of evolutionary computing
, pp. 123-131
-
-
Ceravolo, F.1
Felice, M.D.2
Pizzuti, S.3
-
9
-
-
0000863041
-
Neural network residual kriging application for climatic data
-
Demyanov V, Kanevski M, Chernov S, Savelieva E, Timonin V (1998) Neural network residual kriging application for climatic data. J Geogr Inf Decis Anal 2(2):215–232
-
(1998)
J Geogr Inf Decis Anal
, vol.2
, Issue.2
, pp. 215-232
-
-
Demyanov, V.1
Kanevski, M.2
Chernov, S.3
Savelieva, E.4
Timonin, V.5
-
10
-
-
0035579748
-
Wavelet analysis residual kriging vs. neural network residual kriging
-
Demyanov V, Soltano S, Kanevski M, Canu S, Maignan M, Savelieva E, Timonin V, Pisarenko V (2001) Wavelet analysis residual kriging vs. neural network residual kriging. Stoch Env Res Risk Assess 15:18–32
-
(2001)
Stoch Env Res Risk Assess
, vol.15
, pp. 18-32
-
-
Demyanov, V.1
Soltano, S.2
Kanevski, M.3
Canu, S.4
Maignan, M.5
Savelieva, E.6
Timonin, V.7
Pisarenko, V.8
-
11
-
-
78650201021
-
A high order neural network to solve crossbar switch problem
-
Ding Y, Dong L, Wang L, Wu G (2010) A high order neural network to solve crossbar switch problem. In: Wong KW, Mendis BSU, Bouzerdoum A (eds) ICONIP 2010, part II. LNCS, vol 6444, Australia, pp 692–699
-
(2010)
ICONIP 2010, part II. LNCS
, vol.6444
, pp. 692-699
-
-
Ding, Y.1
Dong, L.2
Wang, L.3
Wu, G.4
-
12
-
-
79960850584
-
Measuring skewness: a forgotten statistic?
-
Doane DP, Seward LE (2011) Measuring skewness: a forgotten statistic? J Stat Educ 19(2):45–63
-
(2011)
J Stat Educ
, vol.19
, Issue.2
, pp. 45-63
-
-
Doane, D.P.1
Seward, L.E.2
-
13
-
-
84877065872
-
Comparison of artificial neural network transfer functions abilities to simulate extreme runoff data
-
Dorofki M, Elshafie AH, Jaafar O, Karim OA, Mastura S (2012) Comparison of artificial neural network transfer functions abilities to simulate extreme runoff data. In: International conference on environment, energy and biotechnology (IPCBEE), vol 33, pp 39–44
-
(2012)
International conference on environment, energy and biotechnology (IPCBEE)
, vol.33
, pp. 39-44
-
-
Dorofki, M.1
Elshafie, A.H.2
Jaafar, O.3
Karim, O.A.4
Mastura, S.5
-
15
-
-
84907695673
-
-
Accessed 2 Apr 2014
-
ESRI (2014) ArcGIS resource center. http://resources.arcgis.com/. Accessed 2 Apr 2014
-
(2014)
ArcGIS resource center
-
-
-
16
-
-
0027627965
-
Working with neural networks
-
Hammerstrom D (1993) Working with neural networks. IEEE Spectr 30(7):46–53
-
(1993)
IEEE Spectr
, vol.30
, Issue.7
, pp. 46-53
-
-
Hammerstrom, D.1
-
17
-
-
84947418173
-
The influence of the sigmoid function parameters on the speed of backpropagation learning
-
Mira J, Sandoval F, (eds), Springer, Berlin:
-
Han J, Moraga C (1995) The influence of the sigmoid function parameters on the speed of backpropagation learning. In: Mira J, Sandoval F (eds) From natural to artificial neural computation. Springer, Berlin, pp 195–201
-
(1995)
From natural to artificial neural computation
, pp. 195-201
-
-
Han, J.1
Moraga, C.2
-
20
-
-
34249996860
-
The extrapolation of artificial neural networks for the modelling of rainfall–runoff relationships
-
Hettiarachchi P, Hall MJ, Minns AW (2005) The extrapolation of artificial neural networks for the modelling of rainfall–runoff relationships. J Hydroinf 7(4):291–296
-
(2005)
J Hydroinf
, vol.7
, Issue.4
, pp. 291-296
-
-
Hettiarachchi, P.1
Hall, M.J.2
Minns, A.W.3
-
21
-
-
0037361264
-
Learning capability and storage capacity of two-hidden-layer feedforward networks
-
Huang GB (2003) Learning capability and storage capacity of two-hidden-layer feedforward networks. IEEE Trans Neural Netw 14(2):274–281
-
(2003)
IEEE Trans Neural Netw
, vol.14
, Issue.2
, pp. 274-281
-
-
Huang, G.B.1
-
23
-
-
80051893176
-
Statistical normalization and back propagation for classification
-
Jayalakshmi T, Santhakumaran A (2011) Statistical normalization and back propagation for classification. Int J Comput Theory Eng 3(1):89–93
-
(2011)
Int J Comput Theory Eng
, vol.3
, Issue.1
, pp. 89-93
-
-
Jayalakshmi, T.1
Santhakumaran, A.2
-
24
-
-
30444441291
-
Rainfall–runoff models using artificial neural networks for ensemble streamflow prediction
-
Jeong DI, Kim YO (2005) Rainfall–runoff models using artificial neural networks for ensemble streamflow prediction. Hydrol Process 19(19):3819–3835
-
(2005)
Hydrol Process
, vol.19
, Issue.19
, pp. 3819-3835
-
-
Jeong, D.I.1
Kim, Y.O.2
-
25
-
-
0006540633
-
Comparing measures of sample skewness and kurtosis
-
Joanes DN, Gill CA (1998) Comparing measures of sample skewness and kurtosis. J R Stat Soc 47(1):183–189
-
(1998)
J R Stat Soc
, vol.47
, Issue.1
, pp. 183-189
-
-
Joanes, D.N.1
Gill, C.A.2
-
27
-
-
84907695177
-
Interpolation of sparse digital elevation model using back propagation neural networks
-
Kachru K, Chamy M, Chowdhury S (2002) Interpolation of sparse digital elevation model using back propagation neural networks. Indian Cartogr 22:219–226
-
(2002)
Indian Cartogr
, vol.22
, pp. 219-226
-
-
Kachru, K.1
Chamy, M.2
Chowdhury, S.3
-
28
-
-
0002293541
-
Artificial neural networks and spatial estimation of chernobyl fallout
-
Kanevsky M, Arutyunyan R, Bolshov L, Demyanov V, Maignan M (1996) Artificial neural networks and spatial estimation of chernobyl fallout. Geoinformatics 7(1–2):5–11
-
(1996)
Geoinformatics
, vol.7
, Issue.1-2
, pp. 5-11
-
-
Kanevsky, M.1
Arutyunyan, R.2
Bolshov, L.3
Demyanov, V.4
Maignan, M.5
-
29
-
-
0030439751
-
A xerion-based perl program to train a neural network for grid pattern recognition
-
Kao JJ (1996) A xerion-based perl program to train a neural network for grid pattern recognition. Comput Geosci 22(9):1033–1049
-
(1996)
Comput Geosci
, vol.22
, Issue.9
, pp. 1033-1049
-
-
Kao, J.J.1
-
31
-
-
84865760441
-
An artificial neural network model for flood simulation using GIS: Johor River Basin, Malaysia
-
Kia MB, Pirasteh S, Pradhan B, Mahmud AR, Sulaiman WNA, Moradi A (2012) An artificial neural network model for flood simulation using GIS: Johor River Basin, Malaysia. Environ Earth Sci 67(1):251–264
-
(2012)
Environ Earth Sci
, vol.67
, Issue.1
, pp. 251-264
-
-
Kia, M.B.1
Pirasteh, S.2
Pradhan, B.3
Mahmud, A.R.4
Sulaiman, W.N.A.5
Moradi, A.6
-
33
-
-
33746647308
-
Earthquake-induced landslide-susceptibility mapping using an artificial neural network
-
Lee S, Evangelista DG (2006) Earthquake-induced landslide-susceptibility mapping using an artificial neural network. Nat Hazards Earth Syst Sci 6:687–695
-
(2006)
Nat Hazards Earth Syst Sci
, vol.6
, pp. 687-695
-
-
Lee, S.1
Evangelista, D.G.2
-
34
-
-
22044456685
-
An offset error compensation method for improving ANN accuracy when used for position control of precision machinery
-
Lou YF, Brunn P (1998) An offset error compensation method for improving ANN accuracy when used for position control of precision machinery. Neural Comput Appl 7(1):90–95
-
(1998)
Neural Comput Appl
, vol.7
, Issue.1
, pp. 90-95
-
-
Lou, Y.F.1
Brunn, P.2
-
35
-
-
77951175284
-
Methods used for the development of neural networks for the prediction of water resource variables in river systems: current status and future directions
-
Maier HR, Jain A, Dandy GC, Sudheer KP (2010) Methods used for the development of neural networks for the prediction of water resource variables in river systems: current status and future directions. Environ Model Softw 25(8):891–909
-
(2010)
Environ Model Softw
, vol.25
, Issue.8
, pp. 891-909
-
-
Maier, H.R.1
Jain, A.2
Dandy, G.C.3
Sudheer, K.P.4
-
38
-
-
0036529032
-
Artificial neural networks as a method of spatial interpolation for digital elevation models
-
Merwin DA, Cromley RG, Civco DL (2002) Artificial neural networks as a method of spatial interpolation for digital elevation models. Cartogr Geogr Inf Sci 29(2):99–110
-
(2002)
Cartogr Geogr Inf Sci
, vol.29
, Issue.2
, pp. 99-110
-
-
Merwin, D.A.1
Cromley, R.G.2
Civco, D.L.3
-
40
-
-
84907689539
-
Implementation of ANN in GRASS—an example of using ANN for spatial interpolation
-
Netzel P (2011) Implementation of ANN in GRASS—an example of using ANN for spatial interpolation. In: Geoinformatics FCE CTU 2011, Prague, Czech Republic, 19–20 May 2011
-
In: Geoinformatics FCE CTU 2011, Prague, Czech Republic
, pp. 19-20
-
-
Netzel, P.1
-
43
-
-
0013379958
-
-
Accessed 2 Apr 2014
-
NIST/SEMATECH (2012) E-handbook of statistical methods. http://www.itl.nist.gov/div898/handbook/. Accessed 2 Apr 2014
-
(2012)
E-handbook of statistical methods
-
-
-
44
-
-
84874117042
-
Landslide susceptibility mapping using frequency ratio, analytic hierarchy process, logistic regression, and artificial neural network methods at the Inje area, Korea
-
Park S, Choi C, Kim B, Kim J (2013) Landslide susceptibility mapping using frequency ratio, analytic hierarchy process, logistic regression, and artificial neural network methods at the Inje area, Korea. Environ Earth Sci 68(5):1443–1464
-
(2013)
Environ Earth Sci
, vol.68
, Issue.5
, pp. 1443-1464
-
-
Park, S.1
Choi, C.2
Kim, B.3
Kim, J.4
-
45
-
-
84907693831
-
Data mining
-
Patel N (2003) Data mining. In: Lecture note, MIT open courseware. http://ocw.mit.edu/courses/sloan-school-of-management/15-062-data-mining-spring-2003/lecture-notes/NeuralNet2002.pdf. Accessed 2 Apr 2014
-
(2003)
In: Lecture note, MIT open courseware
-
-
Patel, N.1
-
46
-
-
70350074037
-
Landslide risk analysis using artificial neural network model focussing on different training sites
-
Pradhan B, Lee S (2009) Landslide risk analysis using artificial neural network model focussing on different training sites. Int J Phys Sci 4(1):1–15
-
(2009)
Int J Phys Sci
, vol.4
, Issue.1
, pp. 1-15
-
-
Pradhan, B.1
Lee, S.2
-
47
-
-
77954084069
-
Delineation of landslide hazard areas on Penang Island, Malaysia, by using frequency ratio, logistic regression, and artificial neural network models
-
Pradhan B, Lee S (2010a) Delineation of landslide hazard areas on Penang Island, Malaysia, by using frequency ratio, logistic regression, and artificial neural network models. Environ Earth Sci 60(5):1037–1054
-
(2010)
Environ Earth Sci
, vol.60
, Issue.5
, pp. 1037-1054
-
-
Pradhan, B.1
Lee, S.2
-
48
-
-
76749088419
-
Landslide susceptibility assessment and factor effect analysis: backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modeling
-
Pradhan B, Lee S (2010b) Landslide susceptibility assessment and factor effect analysis: backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modeling. Environ Model Softw 25(6):747–759
-
(2010)
Environ Model Softw
, vol.25
, Issue.6
, pp. 747-759
-
-
Pradhan, B.1
Lee, S.2
-
49
-
-
77952010906
-
A GIS-based back-propagation neural network model and its cross-application and validation for landslide susceptibility analyses
-
Pradhan B, Lee S, Buchroithner MF (2010a) A GIS-based back-propagation neural network model and its cross-application and validation for landslide susceptibility analyses. Comput Environ Urban Syst 34(3):216–235
-
(2010)
Comput Environ Urban Syst
, vol.34
, Issue.3
, pp. 216-235
-
-
Pradhan, B.1
Lee, S.2
Buchroithner, M.F.3
-
50
-
-
77952410512
-
Approaches for delineating landslide hazard areas using different training sites in an advanced artificial neural network model
-
Pradhan B, Youssef AM, Varathrajoo R (2010b) Approaches for delineating landslide hazard areas using different training sites in an advanced artificial neural network model. Geospat Inf Sci 13(2):93–102
-
(2010)
Geospat Inf Sci
, vol.13
, Issue.2
, pp. 93-102
-
-
Pradhan, B.1
Youssef, A.M.2
Varathrajoo, R.3
-
51
-
-
84907706299
-
Landslide susceptibility analysis in Jeju using artificial neural network (ANN) and GIS
-
Quan HC, Lee BG, Cho EI (2008) Landslide susceptibility analysis in Jeju using artificial neural network (ANN) and GIS. J Environ Sci 17(6):679–687
-
(2008)
J Environ Sci
, vol.17
, Issue.6
, pp. 679-687
-
-
Quan, H.C.1
Lee, B.G.2
Cho, E.I.3
-
52
-
-
0036899543
-
Artificial neural networks for daily rainfall–runoff modeling
-
Rajurkar MP, Kothyari UC, Chaube UC (2002) Artificial neural networks for daily rainfall–runoff modeling. Hydrol Sci J 47(6):865–877
-
(2002)
Hydrol Sci J
, vol.47
, Issue.6
, pp. 865-877
-
-
Rajurkar, M.P.1
Kothyari, U.C.2
Chaube, U.C.3
-
53
-
-
0028466750
-
Advanced supervised learning in multi-layer perceptrons—from backpropagation to adaptive learning algorithms
-
Riedmiller M (1994) Advanced supervised learning in multi-layer perceptrons—from backpropagation to adaptive learning algorithms. Comput Stand Interfaces 16(3):265–278
-
(1994)
Comput Stand Interfaces
, vol.16
, Issue.3
, pp. 265-278
-
-
Riedmiller, M.1
-
55
-
-
0000646059
-
Learning internal representations by error propagation
-
Rumelhart DE, McClelland JL, (eds), MIT Press, Cambridge:
-
Rumelhart DE, Hinton GE, Williams RJ (1986) Learning internal representations by error propagation. In: Rumelhart DE, McClelland JL, The PDP Research Group (eds) Parallel distributed processing: explorations in the microstructure of cognition. MIT Press, Cambridge, pp 318–362
-
(1986)
Parallel distributed processing: explorations in the microstructure of cognition
, pp. 318-362
-
-
Rumelhart, D.E.1
Hinton, G.E.2
Williams, R.J.3
-
56
-
-
84899914647
-
Comparison of supervised and unsupervised learning algorithms for pattern classification
-
Sathya R, Abraham A (2013) Comparison of supervised and unsupervised learning algorithms for pattern classification. Int J Adv Res Artificial Intell 2(2):34–38
-
(2013)
Int J Adv Res Artificial Intell
, vol.2
, Issue.2
, pp. 34-38
-
-
Sathya, R.1
Abraham, A.2
-
57
-
-
53349177535
-
Programming an artificial neural network tool for spatial interpolation in GIS—a case study for indoor radio wave propagation of WLAN
-
Sen A, Gümüsay MU, Kavas A, Bulucu U (2008) Programming an artificial neural network tool for spatial interpolation in GIS—a case study for indoor radio wave propagation of WLAN. Sensors 8(9):5996–6014
-
(2008)
Sensors
, vol.8
, Issue.9
, pp. 5996-6014
-
-
Sen, A.1
Gümüsay, M.U.2
Kavas, A.3
Bulucu, U.4
-
58
-
-
84908614016
-
State of the art of artificial neural networks in geotechnical engineering
-
Shahin MA, Jaksa MB, Maier HR (2008) State of the art of artificial neural networks in geotechnical engineering. Electron J Geotech Eng 8:1–26
-
(2008)
Electron J Geotech Eng
, vol.8
, pp. 1-26
-
-
Shahin, M.A.1
Jaksa, M.B.2
Maier, H.R.3
-
59
-
-
0030297904
-
Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes
-
Tu JV (1996) Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes. J Clin Epidemiol 49(11):1225–1231
-
(1996)
J Clin Epidemiol
, vol.49
, Issue.11
, pp. 1225-1231
-
-
Tu, J.V.1
-
60
-
-
77955925179
-
A GIS-based neuro-fuzzy procedure for integrating knowledge and data in landslide susceptibility mapping
-
Vahidnia MH, Alesheikh AA, Alimohammadi A, Hosseinali F (2010) A GIS-based neuro-fuzzy procedure for integrating knowledge and data in landslide susceptibility mapping. Comput Geosci 36(9):1101–1114
-
(2010)
Comput Geosci
, vol.36
, Issue.9
, pp. 1101-1114
-
-
Vahidnia, M.H.1
Alesheikh, A.A.2
Alimohammadi, A.3
Hosseinali, F.4
-
62
-
-
0010257013
-
The logic of activation functions
-
Rumelhart DE, McClelland JL, (eds), MIT Press, Cambridge:
-
Williams RJ (1986) The logic of activation functions. In: Rumelhart DE, McClelland JL, The PDP Research Group (eds) Parallel distributed processing: explorations in the microstructure of cognition. MIT Press, Cambridge, pp 423–443
-
(1986)
Parallel distributed processing: explorations in the microstructure of cognition
, pp. 423-443
-
-
Williams, R.J.1
-
63
-
-
0242662161
-
The general inefficiency of batch training for gradient descent learning
-
Wilson DR, Martinez TR (2003) The general inefficiency of batch training for gradient descent learning. Neural Netw 16:1429–1451
-
(2003)
Neural Netw
, vol.16
, pp. 1429-1451
-
-
Wilson, D.R.1
Martinez, T.R.2
-
65
-
-
0034223272
-
Application of artificial neural networks in image recognition and classification of crop and weeds
-
Yang CC, Prasher SO, Landry JA, Ramaswamy HS, Ditommaso A (2000) Application of artificial neural networks in image recognition and classification of crop and weeds. Can Agric Eng 42(3):147–152
-
(2000)
Can Agric Eng
, vol.42
, Issue.3
, pp. 147-152
-
-
Yang, C.C.1
Prasher, S.O.2
Landry, J.A.3
Ramaswamy, H.S.4
Ditommaso, A.5
-
66
-
-
33847316019
-
Forecasting solute breakthrough curves through the unsaturated zone using artificial neural networks
-
Yoon H, Hyun Y, Lee KK (2007) Forecasting solute breakthrough curves through the unsaturated zone using artificial neural networks. J Hydrol 335(1–2):68–77
-
(2007)
J Hydrol
, vol.335
, Issue.1-2
, pp. 68-77
-
-
Yoon, H.1
Hyun, Y.2
Lee, K.K.3
-
67
-
-
78650179085
-
A comparative study of artificial neural networks and support vector machines for predicting groundwater levels in a coastal aquifer
-
Yoon H, Jun SC, Hyun Y, Bae GO, Lee KK (2011) A comparative study of artificial neural networks and support vector machines for predicting groundwater levels in a coastal aquifer. J Hydrol 396(1–2):128–138
-
(2011)
J Hydrol
, vol.396
, Issue.1-2
, pp. 128-138
-
-
Yoon, H.1
Jun, S.C.2
Hyun, Y.3
Bae, G.O.4
Lee, K.K.5
-
68
-
-
77955735474
-
Daily outflow prediction by multilayer perceptron with logistic sigmoid and tangent sigmoid activation functions
-
Zadeh MR, Amin S, Khalili D, Singh VP (2010) Daily outflow prediction by multilayer perceptron with logistic sigmoid and tangent sigmoid activation functions. Water Resour Manag 24(11):2673–2688
-
(2010)
Water Resour Manag
, vol.24
, Issue.11
, pp. 2673-2688
-
-
Zadeh, M.R.1
Amin, S.2
Khalili, D.3
Singh, V.P.4
-
69
-
-
84856297247
-
Improving the prediction accuracy of recurrent neural network by a PID controller
-
Zemouri R, Gouriveau R, Patic PC (2010) Improving the prediction accuracy of recurrent neural network by a PID controller. Int J Syst Appl Eng Dev 4(2):19–34
-
(2010)
Int J Syst Appl Eng Dev
, vol.4
, Issue.2
, pp. 19-34
-
-
Zemouri, R.1
Gouriveau, R.2
Patic, P.C.3
|