neural networks algorithms applications and programming techniques phần 7 pot

... occurred and that the network is now in a production mode. We have an input vector, I, and we would like to find the corresponding y vector. The processing is depicted in Figure 6. 17 and proceeds ... in Figure 6.19. 7. 2 Applications of Self-Organizing Maps 275 0®©@©©©©©®©© ©©®©0©©0©0©0 00©©000®®0©0 ©00000000000 00©0©g)©00®©© 0©0©®®0©0®®© OO©®0©®©®0®® OO000000©©©© Figure 7. 9 T...

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... Pattern Analysis and Machine Intelligence PAMI-6: 72 1 -74 1, 1984. [3] G. E. Hinton and T. J. Sejnowski. Learning and relearning in Boltzmann machines. In David E. Rumelhart and James L. McClelland, ... Cognitive Science 9:1 47- 169, 1985. [2] Stuart Geman and Donald Geman. Stochastic relaxation, Gibbs distribu- tions, and the Bayesian restoration of images. In James A...

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... 45, 72 -79 , 110 categorization, 72 rule III (MRIII), 75 rule II (MRII), 73 , 75 mapping network, 94, 208 matrix Karhunen-Loeve (KL), 17 key-weight, 77 , 78 maximum-likelihood training, 273 McClelland, ... 381 eigenvalue, 67 electrocardiograph, 65 energy function, 1 37 landscape, 177 total, 184 ensemble, 174 canonical, 175 entropy, 172 , 175 maximization of, 183 maxim...

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... 262 Chapter 7 Self-Organizing Maps 263 7. 7 SOM Data Processing 265 7. 2 Applications of Self-Organizing Maps 274 7. 3 Simulating the SOM 279 Bibliography 289 Chapter 8 Adaptive Resonance Theory 2 97 8.1 ... in terms of sequential algorithms. Many problems are not suited to this approach, however, causing us to expend Neural Networks Algorithms, Applications, and Program...

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... Neural Networks and Natural Intelligence. MIT Press, Cambridge, MA, 1988. [9] Stephen Grossberg. Nonlinear neural networks: Principles, mechanisms, and architectures. Neural Networks, 1(1): 17- 62, ... Lippmann. Neural net and traditional classifiers. In Proceedings of the Conference on Neural Information Pro- cessing Systems, Denver, CO, November 19 87. [16]Tarun Kh...

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... counter} Bibliography 87 [8] Rodney Winter and Bernard Widrow. MADALINE RULE II: A training algorithm for neural networks. In Proceedings of the IEEE Second In- ternational Conference on Neural Networks, ... David Andes, Bernard Widrow, Michael Lehr, and Eric Wan. MRIII: A robust algorithm for training analog neural networks. In Proceedings of the International Joint Con...

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... MP2, AAAI 87, Seattle, WA, July 19 87. [6] James McClelland and David Rumelhart. Explorations in Parallel Dis- tributed Processing. MIT Press, Cambridge, MA, 1986. [7] James McClelland and David ... sonar targets. Neural Networks, 1(1) :76 -90, 1988. [4] Robert Hecht-Nielsen. Neurocomputing. Addison-Wesley, Reading, MA, 1990. [5] Geoffrey E. Hinton and Terrence J. Sejnowski....

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... Modeling. Academic Press, Orlando, FL, 19 87. [2] M. R. Garey and D. S. Johnson. Computers and Intractability. W. H. Freeman, New York, 1 979 . [3] Morris W. Hirsch and Stephen Smale. Differential ... Smale. Differential Equations, Dynamical Systems, and Linear Algebra. Academic Press, New York, 1 974 . [4] John J. Hopfield. Neural networks and physical systems with emerge...

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... for F\ units is 0 0 .75 6 0 .75 6 0 .75 6 0 .75 6 0 .75 6 0 0 .75 6 0 .75 6 0 .75 6 0 .75 6 0 .75 6 0 0 .75 6 0 .75 6 0 .75 6 0 .75 6 0 .75 6 1 0 .75 6 0 .75 6 0 .75 6 0 .75 6 0 .75 6 0 0 .75 6 0 .75 6 0 .75 6 0 .75 6 0 .75 6 That completes the ... appear as follows. For F\, 0 0 0 .75 6 0 .75 6 0 .75 6 0 .75 6 0 0 0 .75 6 0 .75 6 0 .75 6 0 .75 6 0 0 0 .75 6 0 .75 6 0 .75 6 0 .75 6 1 0 0 .7...

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... (2.863,10.020,0.400 ,7. 160,5 .72 6)' x = (0.206,0 .72 2,0.0288,0.515,0.412)* v = (2.269 ,7. 942,0.000,5. 672 ,4.538)' u = (0.206,0 .72 2,0.000,0.516,0.413)' p = (0.206,0 .72 2,0.000,0.516,0.413)' q ... however, as both u, and q are now nonzero. Beginning again at w, we find: w = (2.263 ,7. 920,0.100,5.6 57, 4.526)' x = (0.206,0 .72 2,0.009,0.516,0.413)* v = (2.2...

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