Transcription of Neural Network Structures - IEEE
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Neural Network Structures - IEEE
3 Neural Network StructuresThis chapter describes various types of Neural Network Structures that are usefulfor RF and microwave applications. The most commonly used Neural networkconfigurations, known as multilayer perceptrons (MLP), are described first,together with the concept of basic backpropagation training, and the universalapproximation theorem. Other Structures discussed in this chapter includeradial basis function (RBF) Network , wavelet Neural Network , and self-organizingmaps (SOM). Brief reviews of arbitrary Structures for ANNs and recurrentneural networks are also IntroductionA Neural Network has at least two physical components, namely, the processingelements and the connections between them. The processing elements arecalled neurons, and the connections between the neurons are known as link has a weight parameter associated with it. Each neuron receivesstimulus from the neighboring neurons connected to it, processes the informa-tion, and produces an output.
Neural Network Structures 65 Figure 3.2 Multilayer perceptrons (MLP) structure. Suppose the total number of layers is L.The 1st layer is the input layer, the Lth layer is the output layer, and layers 2 to L −1 are hidden layers.
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