A multilayer neural adaptive network as a model of nonlinear plants

Summary form only given. A multilayer neural adaptive network is chosen as a model for complex nonlinear plants. The basic element of the neural network (NN) is the sigmoid perceptron. The tuning of NN weights is based on measurements of the vector input and output of a nonlinear plant, the output of which is affected by an additive random noise. Optimal convergence rate algorithms for tuning the NN weights have been obtained.<
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