Abstract

An important feature of neural network processing lies in a network’s ability to adapt to a given problem. The adaptation is accomplished by modifying its internal structure through some learning procedure. Neural network models may be classified in one of two types: The learning may be supervised by someone or something that indicates to the network what is expected of it, or the network may be governed by a self-organizing process in which it automatically develops an internal state that reflects the properties of its input environment. Self-organizing systems need no a priori knowledge supplied by a supervisor, and are particularly valuable when the task of the system depends only upon some property of the input data itself.

© 1991 Optical Society of America