Abstract
Cellular nonlinear/neural networks (CNN's) are one of the analog systems that is hard to emulate or simulate on digital systems. It is known that CNN systems are linear for Gabor-type spatial filters. Although it is possible to represent the state equations of the discrete CNN in matrix notation, it is almost impossible to implement the huge state matrix on a digital system without optimization. In this paper some well known linear equation solving methods are optimized for CNN and required computational powers and memories are compared.
Keywords
Cellular neural network
Computer science
Artificial neural network
Matrix (chemical analysis)
Linear system
Nonlinear system
State (computer science)
Algorithm
Artificial intelligence
Mathematics
Subject Areas
Neural Networks Stability and Synchronization
·Computer Networks and Communications ·Physical Sciences
Neural Networks and Applications
·Artificial Intelligence ·Physical Sciences
Cellular Automata and Applications
·Computational Theory and Mathematics ·Physical Sciences
Loading…