Self-Organizing Systems-A Review and Commentary

Abstract

The class of self-organizing systems represented by networks which learn to recognize patterns is reviewed from an historical standpoint, and some of the behavioral similarities between such nets and biological nervous systems are discussed. Examples and results of several experimental models for alphanumeric character recognition are presented. The network synthesis problem is then recast in terms of redundant information removal, multivariable curve-fitting and expansion in orthonormal functions. Recognition network structures and the learning process are described from these points of view. The potential component and behavioral advantages to be gained from sequential feedback networks are discussed briefly.