Titlebook: Neural Networks; An Introduction Berndt Müller,Joachim Reinhardt Textbook 19901st edition Springer-Verlag Berlin Heidelberg 1990 Konnektion

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Stochastic Neuronsth probability .(±..):.where the activation function .(.) must have the limiting values .(. → −∞) = 0, .(. → −∞) = 1. Between these limits the activation function must rise monotonously, smoothly interpolating between 0 and 1. Such functions are often called . functions.

Perceive

Multilayered Perceptronsote the states of the hidden neurons by the variables .., (. = 1,..., ..).. The synaptic connections between the hidden neurons and the output neurons are denoted by ..; those between the input layer and the hidden layer by .. The threshold potentials of the output neurons are called ϑ.; those of the hidden neurons are called ..

左右连贯

Statistical Physics and Spin Glasses have caught the attention of physicists and have been studied closely during the last decade. In the following chapters we will make ample use of the results and the methods developed in the course of these investigations. Despite the dose magnetic analogy, however, we will always keep in mind that we intend to describe neural networks.

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羽饰

Textbook 19901st editiondation stone of ontological philosophy. Others have taken the human mind as evidence of the existence of supernatural powers, or even of God. Serious scientific in­ vestigation, which began about half a century ago, has partially answered some of the simpler questions (such as how the brain processe

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Network Architecture and Generalizationl in the operational stage. Instead of learning salient features of the underlying input-output relationship, the network simply learns to distinguish somehow between the various input patterns of the training set and to associate them with the correct output.

FID

Combinatorial Optimizationtion problems. Here a . has to be minimized, which depends on the order of a finite number of objects. The number of arrangements of . objects, and therefore the effort to find the minimum of ., grows exponentially with ..

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