Titlebook: Artificial Neural Networks; International Worksh Alberto Prieto Conference proceedings 1991 Springer-Verlag Berlin Heidelberg 1991 Artifici

極微小

Vulnerable

Zur Vielf?ltigkeit von Geschlechtities for each initial symbol may be explicitly computed. Although thermal noise may muddle the code, we show how it can statistically rid the result of unwanted sequences while maintaining the network accuracy within a given bound.

endoscopy

死亡

Adaptive optimization of neural algorithms,successfully used in Signal Processing, is extended to 2 neural algorithms : Kohonen self-organizing maps and blind separation of sources (Hérault-Jutten algorithm). Although this procedure increases the algorithms complexity, it remains very interesting :

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Stability measurement criterion for neural networks of competitive learning,criterion is based on the network stability measurement in contrast to the weight variation which is defined by Rumelhart et al. Results obtained in a number of realized tests which allow you to evaluate the step number reduction reached when applying the new criterion as contrasted with Rumelharts are shown.

Type-1-Diabetes

On the power of networks of majority functions,ts about the complexity of the circuits composed of such gates are reported. They show that this simple family of functions remains powerful in therm of circuit complexity. The learning problem with this subclass of threshold function is also studied and numerical experiments of different algorithms are reported.

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無(wú)聊的人

Implementing a "psychophysical" pattern classifier in a decrementing network,tions produced by the patterns and the inputs to be classified. We develope the classifying algorithm which is based on a logarithmic distance, and we show how it can be implemented in a network of simple devices. Finally, we compare the performance of this classifier versus a Gaussian classifier in a specific task.

填料

,Contributions of Neural Net’s Theory to the understanding of Psychopathological productions in Schiknowledge in which a Neural Net interpretation of the concepts of Signification and Intention produces a relevant insight into the semantic structure of psychopathological disturbances in Schizophrenia.