ABSTRACT

A strong understanding of the principles and behavior of neural networks is important to understanding wavelet networks. In this chapter, we introduce the general structure and properties of neural networks. We introduce the conceptual basis such as the McCulloch-Pitts model of a neuron and Hebb’s rule. We also present important neural architectures such as multilayer pereeptrons, baekpropagation weight update rule, eompetetive learning and models that build on eompetetive learning, and recurrent neural networks.