17th International Symposium on
Mathematical Theory of Networks and Systems
Kyoto International Conference Hall, Kyoto, Japan, July 24-28, 2006

MTNS 2006 Paper Abstract


Paper TuP01.4

Bavafa-Toosi, Yazdan (Keio Univ.), Ohmori, Hiromitsu (Keio Univ.)

Flexible Neural Network-Based Associative Memories – Part I: Behavior

Scheduled for presentation during the Regular Session "Neural Networks I" (TuP01), Tuesday, July 25, 2006, 16:35−17:00, Room B2

17th International Symposium on Mathematical Theory of Networks and Systems, July 24-28, 2006, Kyoto, Japan

This information is tentative and subject to change. Compiled on June 23, 2024

Keywords Neural networks, Model reference control, Control of nonlinear systems


Abstract——The successful applications of flexible neural networks (FNNs) notwithstanding, nothing was rigorously known about them until recently that we examined them in a theoretical framework. Following those works, two models of FNN-based associative memories (AMs) are introduced and studied from the behavioral standpoint, i.e., model following or continuous trajectory learning. A nonlinearly parameterized (NLP) adaptive control is proposed as the training scheme. The solution contains a time-varying designer-specified vector parameter, and thus some degrees of freedom, which determines the control input (or the teaching signal) and the rate of adaptation (or learning). Its global stability and convergence are proven.