A fast simplified fuzzy ARTMAP network

Neural Processing Letters

Volumn 17, Issue 3, 2003, Pages 273-316

  Vakil Baghmisheh, Mohammad Taghi ^a   Pavešić, Nikola ^a  

^a UNIVERSITY OF LJUBLJANA   (Slovenia)

Author keywords

Backpropagation with plummeting learning rate factor; Backpropagation with selective training; Comparative study; Farsi optical character recognition; Multilayer perceptron; Neural networks; Simplified fuzzy ARTMAP

Indexed keywords

BACKPROPAGATION; COMPUTER SIMULATION; FEATURE EXTRACTION; FUZZY SETS; LEARNING ALGORITHMS; LEARNING SYSTEMS; OPTICAL CHARACTER RECOGNITION;

BACKPROPAGATION WITH PLUMMETING LEARNING RATE FACTOR; BACKPROPAGATION WITH SELECTIVE TRAINING; FARSI OPTICAL CHARACTER RECOGNITION;

MULTILAYER NEURAL NETWORKS;

EID: 0242653720     PISSN: 13704621     EISSN: None     Source Type: Journal    
DOI: 10.1023/A:1026004816362     Document Type: Letter

References (52)

1. McCulloch, W. S. and Pitts, W.: A logical calculus of the ideas imminent in nervous activity, Bulletin of Mathematical Biophysics, 5 (1943), 115-133.
2. Grossberg, S.: Adaptive pattern classification and universal recoding, II: feedback, expectation, olfaction and illusions, Biological Cybernetics, 23 (1976), 187-202.
3. Carpenter, G. A. and Grossberg, S.: A massively parallel architecture for a self organizing neural pattern recognition machine, Computer Vision, Graphics and Image Processing, 37 (1987), 54-115.
4. Carpenter, G. A. and Grossberg, S.: ART2: Self-organization of stable category recognition codes for analog input patterns, Applied Optics, 26(23) (1987), 4919-4930.
5. Carpenter, G. A. and Grossberg, S.: ART3: Hierarchical search using chemical transmitters in self-organizing pattern recognition architecture, Neural Networks, 3 (1990), 129-152.
6. Carpenter, G. A., Grossberg G. S. and Reynolds J. H.: ARTMAP: Supervised real-time learning and classification of mon-stationary data by a self-organizing neural network, Neural Networks, 4 (1991), 565-588.
7. Carpenter, G. A., Grossberg, S. and Rosen, D. B.: Fuzzy ART: Fast stable learning and categorization of analog patterns by an adaptive resonance system, Neural Networks, 4 (1991), 759-771.
8. Carpenter, G. A., Grossberg, S., Markuzon, N., Reynolds, J. H. and Rosen, D. B.: Fuzzy ARTMAP: A neural network architecture for incremental supervised learning of analog multidimensional maps, IEEE Trans. on Neural Networks, 3(5) (1992), 697-713.
9. Carpenter, G. A., Grossberg, S. and Rosen, D. B.: ART2-A: An Adaptive Resonance Algorithm for Rapid Category Learning and Recognition, Neural Networks, 4, (1991), 493-504.
10. Carpenter, G. A. and Grossberg, S.: A self-organizing neural network for supervised learning, recognition, and prediction, IEEE Communications Mag., pp. 38-49, Sep. 1992.
11. Kasuba, T.: Simplified Fuzzy ARTMAP, AI Expert, 8(11) (1993), 18-25.
12. Marriott, S. and Harrison, R. F.: A modified fuzzy ARTMAP architecture for the approximation of noisy mappings, Neural-Networks, 8(4) (1995), 619-641.
13. Malkani, A. and Vassiliadis, C. A.: Parallel implementation of the fuzzy ARTMAP neural network paradigm on a hypercube, Expert Systems, 12(1) (1995), 39-53.
14. Chen, B. and Hoberock, L. L.: A Fuzzy Neural Network Architecture for Fuzzy Control and Classification, ICNN 96, The 1996 IEEE International Conference on Neural Networks (Cat. No. 96CH35907), IEEE, 2, pp. 1168-1173 New York, USA, 1996.
15. Blume, M., Van-Blerkom, D. A. and Esener, S. C.: Fuzzy ARTMAP Modifications for Intersecting Class Distributions, WCNN'96, World Congress on Neural Networks, International Neural Network Society 1996 Annual Meeting, pp. 250-255. Lawrence Erlbaum Assoc, USA, 1996.
16. Chee, P. L. and Harrison, R. F.: Modified fuzzy ARTMAP approaches bayes optimal classification rates: An empirical demonstration, Neural Networks, 10(4) (1997), 755-774.
17. Srinivasa, N.: Learning and generalization of noisy mappings using a modified PROBART neural network, IEEE Trans. Sign. Proc., 45(10) (1997), 2533-2550.
18. Hsien, L. T. and Shie, J. L.: A Neural Network Model for Spoken Word Recognition, IEEE International Conference on Systems, Man, and Cybernetics, Computational Cybernetics and Simulation (Cat. no. 97CH36088-5), IEEE, 5, pp. 4029-4034 New York, USA, 1997.
19. Carpenter, G. A. and Markuzon, N.: ARTMAP-IC and medical diagnosis: Instance counting and inconsistent cases, Neural Networks, 11(2) (1998), 323-336.
20. Healy, M. J. and Caudell, T. P.: Guaranteed two-Pass convergence for supervised and inferential learning, IEEE Tran. Neural Networks, 9(1) (1998), 195-204.
21. Jervis, B. W., Garcia, T. and Giahnakis, E. P.: Probabilistic simplified fuzzy ARTMAP (PSFAM) and application to biosignal data, IEE Proc. Science Measur. and Tech., 146(4) (1999), 165-169.
22. Verzi, S. J., Heileman, G. L., Georgiopoulos, M. and Healy, M. J.: Boosted ARTMAP, IEEE International Joint Conference on Neural Networks Proce. IEEE World Congress on Computational Intelligence (Cat. no. 98CH36277), IEEE, 1, 1, pp. 396-401 New York, USA, 1998.
23. Dagher, I., Georgiopoulos, M., Heileman, G. L. and Bebis, G.: Fuzzy ARTVar: An Improved Fuzzy ARTMAP Algorithm, IEEE International Joint Conference on Neural Networks Proc. IEEE World Congress on Computational Intelligence (Cat. no. 98CH36227). IEEE, 3, pp. 1688-1693 New York, USA, 1998.
24. Werbos, P. J.: Beyond Regression: New Tools for Prediction and Analysis in Behavioral Science, Ph.D Thesis, Harvard University, Cambridge, MA., 1974.
25. Parker, D.B. Learning Logic, Invention Report s81-64, File 1, Office of Technology Licensing, Stanford University, March 1982.
26. Rumelhart, D. E. and McClelland, J. L.: Parallel Distributed Processing: Exploration in the Microstructure of Cognition, MIT press, 1986.
27. Jondarr, G.: Backpropagation Family Album, Technical Report C/TR96-05, Department of Computing, Macquarie University, New South Wales, August, 1996.
28. Aggarwal, R. K., Xuan, Q. Y., Johns, A. T., Furong, L. I. and Benett, A.: A novel approach to fault diagnosis in multicircuit transmission lines using fuzzy ARTMAP neural networks, IEEE Trans. on Neural networks, 10(5) (1999), 1214-1221.
29. Gardner, J. W., Shin, H. W., Hines, E. L. and Dow, C. S.: An electronic nose system for monitoring the quality of potable water, Sensors and Actuators B (Chemical), B69(3) (2000), 336-341.
30. Heinke, D. and Hamker, F. H.: Comparing neural networks: a benchmark on growing neutral gas, growing cell structures, and fuzzy ARTMAP, IEEE Trans. on Neural Networks, 9(6) (1998), 1279-1291.
31. Hines, W. L., Llobet, E. and Gardner, J. W.: Neural network based electronic nose for apple ripeness determination, Electronics Letters, 35(10) (1999), 821-823.
32. Keyvan, S., Xiaolong, S. and Kell, M.: Nuclear fuel, pellet inspection using artificial neural networks, Journal of Nuclear Materials, 264(12) (1999), 141-154.
33. Liobet, E., Hines, E. L., Gardner, J. W., Bartlett, P. N. and Mottram, T. T.: Fuzzy ART-MAP based electronic nose data analysis, Sensors and Actuators B (Chemical), B61(13) (1999), 183-190.
34. Ludwig, L., Sapozhnikova, E., Lunin, V. and Rosenstiel, W.: Error classification and yield prediction of chips in semiconductor industry applications, Neural Computing & Applications, 9(3) (2000), 202-210.
35. Mills, H., Burton, D. R. and Lalor, M. J.: Applying backpropagation neural networks to fringe analysis, Optics and Lasers in Engineering, 23(5) (1995), 33-41.
36. Muchoney, D. and Williamson, J.: A Gaussian adaptive resonance theory neural network classification algorithm applied to supervised land cover mapping using multitemporal vegetation index data, IEEE transactions on Geoscience and Remote Sensing, 39(9) (2001), 1969-1977.
37. Obaidat, M. S., Khalid, H. and Sadoun, B.: Ultrasonic transducer characterization by neural networks, Information Sciences, 107(14) (1998), 195-215.
38. Obaidat, M. S. and Sadoun, B.: Verification of computer users using keystroke dynamics, IEEE Trans. on Systems, Man and Cybernetics, Part B (Cybernetics), 27(2) (1997), 261-269.
39. Raveendran, P., Palaniappan, R. and Omatu, S.: Fuzzy ARTMAP classification of invariant features derived using angle of rotation from a neural network, Information Sciences, 130(14) (2000), 67-84.
40. Shin, H. W., Llobet, E., Gardner, J. W., Hines, E. L. and Dow, C. S.: Classification of the strain and growth phase of cyanobacteria in potable water using an electronic nose system, IEE Proc. Science, Measurement and Technology, 147(4) (2000), 158-164.
41. Walczak, S.: Neural network models for a resource allocation problem, IEEE Trans. on Systems, Man and Cybernetics, Part B (Cybernetics), 28(2) (19998), 276-284.
42. Song, X. H., Hopke, P. K., Bruns, M. A., Bossio, D. A. and Scow, K. M.: A fuzzy adaptive resonance theory supervised predictive mapping neural network applied to the classification of multivariate chemical data, Chemometrics and Intelligent Laboratory Systems, 41(2) (1998), 161-170.
43. Zhang, Z. and Manikopoulos, C.: Neural networks in statistical anomaly intrusion detection, Neural Network World, 11(3) (2001), 305-316.
44. Carpenter, G. A., Gjaja, M. N., Gopal, S. and Woodcock, C. E.: ART Neural Networks for Remote Sensing: Vegetation Classification from Landsat TM and Terrain Data, IEEE Trans. on Geoscience and Remote Sensing, 39(2), (1997), 308-325.
45. Rojas, R.; Neural Networks: A Systematic Introduction, New York, Springer-Verlag, 1996.
46. Fahlman, S. C. and Lebiere, C.: The cascade-correlation learning architecture, Technical Report, CMU-CS-90-100, Carnegie Mellon University, Pittsburgh, 1988.
47. Fahlman, S. E.: An empirical study of learning speed in backpropagation networks, Technical Report, CMU-CS-88-162, Carnegie Mellon University, Pittsburgh, 1988.
48. Haykin, S.: Neural Networks: A Comprehensive Foundation, USA, Macmillan, 1994.
49. Theodoridis, S. and Koutroumbas, K. Pattern Recognition, USA, Academic Press, 1999.
50. Vakil, M.T. and Pavešic, N. Backpropagation with declining learning rate, In: Proc. of the 10th Electrotechnical and Computer Science Conference, pp. 297-300. Portorož, Slovenia, B, 2001.
51. Vakil, M.-T.: Farsi Character Recognition Using Artificial Neural Networks, Ph.D Thesis, Faculty of Electrical Engineering, University of Ljubljana, October 2002.
52. Diamantaras, K. I. and Kung, S. Y.: Principal Component Neural Networks: Theory and Applications, John Wiley & Sons, 1996.