neural networks algorithms applications and programming techniques phần 3 docx
neural networks algorithms applications and programming techniques phần 3 docx
... W 23 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 ... 13 14 15 "22 ^ 23 ^24 W 25 W 32 ^33 ^34 ^35 ^42 W 43 W 44 W 45 W...
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neural networks algorithms applications and programming techniques phần 1 ppt
... Spatiotemporal Networks (STNS) 34 5 Contents xiii 9 .3 The Sequential Competitive Avalanche Field 35 5 9.4 Applications of STNS 36 3 9.5 STN Simulation 36 4 Bibliography 37 1 Chapter 10 The Neocognitron 37 3 10.1 ... Description 2 93 8.2 ART1 298 8 .3 ART2 31 6 8.4 The ART1 Simulator 32 7 8.5 ART2 Simulation 33 6 Bibliography 33 8 Chapter 9 Spatiotemporal Pattern Classifi...
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neural networks algorithms applications and programming techniques phần 2 pptx
... Eckmiller and Christoph v. d. Malsburg, editors. Neural Computers. NATO ASI Series F: Computer and Systems Sciences. Springer-Verlag, Berlin, 1988. [8] Stephen Grossberg, editor. Neural Networks and ... removal of a random noise from a constant signal. The constant signal level is C = 3, and the random noise signal has a constant power, (r 2 ) — n — 0.025. Assume that th...
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neural networks algorithms applications and programming techniques phần 4 ppt
... to the y layer, and update the values on the y-layer units. We shall see how this propagation is done shortly. 3 3. Propagate the updated y information back to the x layer and update the units ... the momentum term discussed in Section 3. 4 .3. Specifically, alpha is the momentum parameter, and delta refers to the weight change values; see Eq. (3. 24). procedure adjust_wei...
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neural networks algorithms applications and programming techniques phần 5 pdf
... Hopfield and David W. Tank. Computing with neural circuits: A model. Science, 233 :625- 633 , August 1986. [8] Bart Kosko. Adaptive bidirectional associative memories. Applied Optics, 26( 23) :4947-4960, ... 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...
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neural networks algorithms applications and programming techniques phần 6 potx
... Cambridge, MA, pages 614- 634 , 1988. Reprinted from IEEE Transactions of Pattern Analysis and Machine Intelligence PAMI-6: 721-741, 1984. [3] G. E. Hinton and T. J. Sejnowski. Learning and relearning ... Learning in parallel networks. Byte, 10(4):265-2 73, April 1985. [5] S. Kirkpatrick, Jr., C. D. Gelatt, and M. P. Vecchi. Optimization by sim- ulated annealing. In James A....
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neural networks algorithms applications and programming techniques phần 7 pot
... and 12 different input vectors are used to train the network. These 12 vectors represent images of the shuttle at 30 -degree incre- ments (0°, 30 °, , 33 0°). Since there are 12 categories and ... (0,1) (0,2) (0 ,3) (0,4) o o p o o o p l eoe -0+ o o o cf p G o o o ( § )p e o o o o o w, f (c) (1,0) KTO O O O (2 ,3) *, -Q-Q- 0-0 O (3, 3) w -OQ €>KD O O O O O O Figur...
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neural networks algorithms applications and programming techniques phần 8 ppsx
... 0.756 0.756 0.756 For F 2 , 00010 00001 0 .32 9 0 .32 9 0 .32 9 0 .32 9 0 .32 9 0 .32 9 0 .32 9 0 .32 9 0 .32 9 0 .32 9 0 .32 9 0 .32 9 0 .32 9 0 .32 9 0 .32 9 0 .32 9 0 .32 9 0 .32 9 0 .32 9 0 .32 9 If we return to the superset vector, ... looks like 00010 0 0 0.75 0 0.75 0 .32 9 0 .32 9 0 .32 9 0 .32 9 0 .32 9 0 .32 9 0 .32 9 0 .32 9 0 .32 9 0 .32 9 0 .32 9 0 .32 9 0 .32 9 0 .32 9 0 .3...
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neural networks algorithms applications and programming techniques phần 9 pptx
... 2.0 63 7.220 0.000 5.157 4.126 \ 2. 236 2. 236 2. 236 2. 236 2. 236 2. 236 2. 236 2. 236 2. 236 2. 236 2. 236 2. 236 2. 236 2. 236 2. 236 2. 236 2. 236 2. 236 2. 236 2. 236 \ 2. 236 2. 236 2. 236 2. 236 2. 236 / 32 4 ... following equations: w, = Ii+ aui (8 .33 ) Xl = e +L\\ (8 ' 34 ) v t = /(I*) + bf(q t ) (8 .35 ) (8 .36 ) (yi)zn (8 .37 ) q t = —p-77 (8 .38...
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neural networks algorithms applications and programming techniques phần 10 potx
... 2 63 visual (striate), 37 3 layer IV, 37 5 layer III, 37 5 layer II, 37 5 cost function, 148 Cottrell, G. W., 124 Counterpropagation network (CPN), 2 13- 262, 264, 265, 2 73, 286, 294, 33 1, 34 3, 37 0 architecture ... 182, 184 Grajski, Kamil, 37 1 grandmother cell, 37 5 Grossberg, Stephen, 228, 230 , 232 , 248, 262, 292, 2 93, 297, 299, 31 6, 33 7, 34 2 Hamming distance,...
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