@MISC{Neural_feedforwardmultilayer, author = {Feedforward Multilayer Neural and J J(w}, title = {Feedforward Multilayer Neural Networks}, year = {} }

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Abstract

tput) is very commonly used to approximate unknown mappings. If the output layer is linear, such a network may have a structure similar to an RBF network. 5.1 Multilayer perceptrons Multilayer perceptrons are commonly used to approximate complex nonlinear mappings. In general, it is possible to show that two layers are sufficient to approximate any nonlinear function. Therefore, we restrict our considerations to such two-layer networks. The structure of the decoding part of the two-layer back-propagation network is presented in Figure (5--2). ## # W # p Hidden layer Output layer Figure 5--2: A block-diagram of a single-hidden-layer feedforward neural network The structure of each layer has been depicted in Figure ??. Nonlinear functions used in the hidden layer and in the output layer can be different. There are two weight matrices: an L p matrix W in the hidden layer, and an m L matrix W in the output layer. The working of the network can be described i