Neural Networks for RF and Microwave Design
An electrical engineering textbook on practical applications of artificial intelligence technology methods to solving complex radio-frequency and microwave design problems.
TABLE OF CONTENTS Neural Networks for RF and Microwave Design
Chapter 3: Neural Network Structures
This chapter describes various types of neural network structures that are useful for RF and microwave applications. The most commonly used neural network configurations, known as multilayer perceptrons (MLP), are described first, together with the concept of basic backpropagation training, and the universal approximation theorem. Other structures discussed in this chapter include radial basis function (RBF) network, wavelet neural network, and self-organizing maps (SOM). Brief reviews of arbitrary structures for ANNs and recurrent neural networks are also included.
3.1 Introduction
A neural network has at least two physical components, namely, the processing elements and the connections between them. The processing elements are called neurons, and the connections between the neurons are known as links. Every link has a weight parameter associated with it. Each neuron receives stimulus from the neighboring neurons connected to it, processes the information, and produces an output. Neurons that receive stimuli from outside the network (i.e., not from neurons of the network) are called input neurons. Neurons whose outputs are used externally are called output neurons. Neurons that receive stimuli from other neurons and whose output is a stimulus for other neurons in the neural network are known as hidden neurons. There are different ways in which information can be processed by a neuron, and different ways of connecting the neurons to one another. Different neural network structures can be constructed by using different processing elements and by the specific manner in which they are connected.
A variety of neural network structures have been developed for...
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