Artificial Neural Networks — ICANN'97: 7th International by Wolfram Schultz (auth.), Wulfram Gerstner, Alain Germond,

By Wolfram Schultz (auth.), Wulfram Gerstner, Alain Germond, Martin Hasler, Jean-Daniel Nicoud (eds.)

This e-book constitutes the refereed complaints of the seventh foreign convention on man made Neural Networks, ICANN'97, held in Lausanne, Switzerland,in October 1997. The 201 revised papers offered have been chosen from numerous submissions and provides a special documentation of the state-of-the-art within the zone. The papers are equipped in elements on coding and studying in biology; cortical maps and receptive fields; studying: idea and purposes; sign processing: blind resource separation, vector quantization, and self-organization; robotics, independent brokers, and regulate; speech, imaginative and prescient and development attractiveness; prediction, forecasting and tracking; and implementations.

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Extra resources for Artificial Neural Networks — ICANN'97: 7th International Conference Lausanne, Switzerland, October 8–10, 1997 Proceeedings

Example text

The neural network used to predict a chaotic time sequence generated by Eq. (12). 0 Xt Fig. 2. Prediction of Xt+ 1 given Xt. The continuous curve represents Eq. 913, and the circles refer to the prediction of the network after learning. 1 0 500 I 510 520 I 530 t Fig. 3. Long term prediction capability of the neural network. Starting from X500, the network recursively generates X501, X502, ... without readjustment of the current Xt-value, and the resulting prediction error is plotted as a function of t.

N} in I~n. Given the structure (W,T), to each neuron there is associated a Boolean linearly separable function [Hu65] fi : {0,1 }n---~{0,1} given by n fi(2:l ..... l:n) = H( ~ wij'Cj - ti) j=l where H(x) = { 0 ifif x>0 x<0 Of the many iteration modes which have been defined for a network [Mc43, Ho82, Fo85, Ro87, Be89], here we mention only the parallel and the sequential one. If a network operates according to the parallel mode its evolution from the initial state 32 ~(0) = ('Cl(O)..... '~n(O)) is given by: xi(t+l)=fi(xl(t ) .....

We now s u m m a r i z e t h e paper. In Section I we give a s h o r t d e s c r i p t i o n of t h e c h a r a c t e r i s t i c p r o p e r t i e s of our model and explain its usefulness for epidemiological r e s e a r c h . We d i s c u s s b r i e f l y t h e main d i f f e r e n c e s between o u r model and t h e s t a n d a r d epidemiological models and list t h e problems and q u e s t i o n s to which t h e model is able to g i v e q u i t e accurate answers. Section II is d e v o t e d to t h e general u n d e r l y i n g m a t h e m a t i c a l s t r u c t u r e , We i n t r o d u c e t h e r e l e v a n t s p a c e s of random g r a p h s and explain t h e c o n s t r u c t i o n of t h e d i s c r e t e s t o c h a s t i c p r o c e s s describing t h e s p r e a d of t h e i n f e c t i o n .

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