Algorithms and Implementation Architectures for Hebbian Neural Networks

Abstract

Systolic architectures for Sanger and Rubner Neural Networks (NNs) are proposed, and the local stability of their learning rules is taken into account based on the indirect Lyapunov method. In addition, these learning rules are improved for applications based on Principal Component Analysis (PCA). The local stability analysis and the systolic architectures for Sanger NN and Rubner NN are presented in a common framework.