neural networks algorithms applications and programming techniques phần 4 ppt

... 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. Neural network ... Sejnowski and Charles R. Rosenberg. Parallel networks that learn to pronounce English text. Complex Systems, 1: 145 -168, 1987. [10] Philip D. Wasserman. Neural Computing: Theory...

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... in terms of sequential algorithms. Many problems are not suited to this approach, however, causing us to expend Neural Networks Algorithms, Applications, and Programming Techniques James A. Freeman David ... understanding of the operation of the specific networks presented • The ability to program simulations of those networks successfully • The ability to apply neural ne...

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... Neurocomputer applications. In Rolf Eckmiller and Christoph v.d. Malsburg, editors, Neural Computers, pages 44 5 -45 4. Springer-Verlag, 1988. NATO ASI Series F: Computers and System Sci- ences Vol. 41 . [12] ... neural net and conventional classifiers. In Proceedings of the IEEE First Interna- tional Conference on Neural Networks, San Diego, CA, pp. iv .48 5-iv .49 4 June...

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... of T = (4. 151 ,4. 151 ,4. 151 ,4. 151 ,4. 151 ,4. 151)' Because all of the activities are the same, the first unit becomes the winner and the activity vector becomes T = (4. 151,0,0,0,0,0)' and the ... (0.206,0.722,0.0288,0.515,0 .41 2)* v = (2.269,7. 942 ,0.000,5.672 ,4. 538)' u = (0.206,0.722,0.000,0.516,0 .41 3)' p = (0.206,0.722,0.000,0.516,0 .41 3)' q = (0....

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... W 24 W 32 W 33 W 34 W 45 W 42 W 43 W 52 W 53 W 44 W 32 WWW 33 34 35 W W 42 43 W W A , 44 45 Weight matrix: 5th row, 5th column ~ W 55 W 45 W 35 W 25 W 15 W 54 W 44 W 34 W 24 ^ 14 W 53 W 43 W 33 W 23 W ,3 ^52 W 42 W 32 W 22 WK W S\ W 41 W 3\ tv 21 ^11 Figure ... w w 12 13 14 15 "22 ^23 ^ 24 W 25 W 32 ^33 ^ 34 ^35 ^42 W 43 W 44 W 4...

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... Morris W. Hirsch and Stephen Smale. Differential Equations, Dynamical Systems, and Linear Algebra. Academic Press, New York, 19 74. [4] John J. Hopfield. Neural networks and physical systems ... have u X i(t + 1) = uxitt) + &u xt (4. 33) and vxt = 9Xr(u X i) = ^(l + tanh(Awxi)) (4. 34) If we substitute TXI.YJ from Eq. (4. 30) into Eq. (4. 31), and define...

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... Cambridge, MA, pages 6 14- 6 34, 1988. Reprinted from IEEE Transactions of Pattern Analysis and Machine Intelligence PAMI-6: 721- 741 , 19 84. [3] G. E. Hinton and T. J. Sejnowski. Learning and relearning ... Cognitive Science 9: 147 -169, 1985. [2] Stuart Geman and Donald Geman. Stochastic relaxation, Gibbs distribu- tions, and the Bayesian restoration of images. In Jam...

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... of the problem. 6 .4 The CPN Simulator 249 outs weights Figure 6. 24 The complete data structure for the CPN is shown. These structures are representative of all the layered networks that we simulate ... television monitor, and robot all interface to a desktop computer that simulates the neural network and houses a video frame-grabber board. The architecture is an example of how a...

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... various learning methods for neural networks, as well as a review of the necessary mathematics. Bibliography [1] Willian Y. Huang and Richard P. Lippmann. Neural net and traditional classifiers. ... Sciences. Springer-Verlag, New York, 19 84. [3] Teuvo Kohonen. The " ;neural& quot; phonetic typewriter. Computer, 21(3): 11-22, March 1988. [4] H. Ritter and K. Schulten. Topol...

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... 2 64 Hopfield, John J., 41 , 127, 155, 157, 167, 169, 190 Hopfield memory (network), 127, 141 -157, 170, 178 continuous, 141 , 144 , 146 discrete, 141 high-gain limit, 150 Huang, William Y., 41 , ... output, 227, 229 quenching, 19 sigmoid, 98, 99, 103, 1 14, 144 , 145 , 146 , 227, 230, 319 gain, 19, 392 gain parameter, 144 , 145 , 148 Gaussiin function, 278 Gelatt.lc. D., 212...

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