Neural network concepts and paradigms

This chapter provides an overview of neural networks. Neural networks consist of processing elements and weighted connections. Each layer in a neural network consists of a collection of processing elements. Each processing element (PE) collects the values from all of its input connections, performs a predefined mathematical operation, and produces a single output value. The combination of processing elements and weighted connections creates a neural network topology. A convenient analogy is a directed graph, where the edges are analogous to the connection weights and the nodes are analogous to the processing elements. Neural networks cannot operate without data. Some neural networks use only single patterns and others use pattern pairs. The dimensionality of the input pattern is not necessarily the same as the output pattern. When a network uses only single patterns, it is defined as an autoassociative network, and when a network uses pattern pairs, it is heteroassociative.