7 The Backpropagation Algorithm - fu-berlin.de
R. Rojas: Neural Networks, Springer-Verlag, Berlin, 1996 7 The Backpropagation Algorithm 7.1 Learning as gradient descent We saw in the last chapter that …
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11 Fuzzy Logic - Freie Universität
page.mi.fu-berlin.de292 11 Fuzzy Logic Crisp sets are a special case of fuzzy sets, since the range of the function is restricted to the values 0 and 1. Operations defined over crisp sets, such as union or intersection, can be generalized to cover also fuzzy sets. Assume as an example that X = {x1,x2,x3}.
4 Perceptron Learning - fu-berlin.de
page.mi.fu-berlin.de“learning”. A learning algorithm must adapt the network parameters accord-ing to previous experience until a solution is found, if it exists. 4.1.1 Classes of learning algorithms Learning algorithms can be divided into supervised and unsupervised meth-ods. Supervised learning denotes a method in which some input vectors are
13 The Hopfield Model - fu-berlin.de
page.mi.fu-berlin.dethe properties of neural networks lacking global synchronization. Networks in which the computing units are activated at different times and which provide a computation after a variable amount of time are stochas-tic automata. Networks built from this kind of units behave likestochastic dynamical systems. 13.1.2 The bidirectional associative ...
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