Many similarities exist between the class of nonlinear filters called stack filters and neural networks. In this chapter, we describe the relationships between these areas and how the theory of stack filters may be applied to the study of neural nets. The concept of a neural network represents one of the most original ideas ever to appear in the field of computing and artificial intelligence. However, many of the tools used to study these architectures are not innovative, but are the same tools used to analyze more conventional systems. Some of these tools are the mean squared error measure, orthogonality of functions, and optimization by minimizing energy, and correlation techniques. These are the same tools that have largely failed modern Al as well as the study of nonlinear systems. We believe these classical tools of linear theory will be of limited use in the study of neural systems.

Date Published: 27 December 1990
PDF: 19 pages
Proc. SPIE 1347, Optical Information Processing Systems and Architectures II, (27 December 1990); doi: 10.1117/12.23425

Published in SPIE Proceedings Vol. 1347:
Optical Information Processing Systems and Architectures II
Bahram Javidi, Editor(s)