Continuous action reinforcement learning automata and their application to adaptive digital filter design

Howell, M. N. and Gordon, Timothy (2001) Continuous action reinforcement learning automata and their application to adaptive digital filter design. Engineering Applications of Artificial Intelligence, 14 (5). pp. 549-561. ISSN 0952-1976

Full content URL: http://www.sciencedirect.com/science/article/pii/S...

Full text not available from this repository.

Item Type:Article
Item Status:Live Archive

Abstract

In the design of adaptive IIR filters, the multi-modal nature of the error surfaces can limit the use of gradient-based and other iterative search methods. Stochastic learning automata have previously been shown to have global optimisation properties making them suitable for the optimisation of filter coefficients. Continuous action reinforcement learning automata are presented as an extension to the standard automata which operate over discrete parameter sets. Global convergence is claimed, and demonstrations are carried out via a number of computer simulations. © 2002 Elsevier Science Ltd. All rights reserved.

Keywords:Adaptive filtering, Computer simulation, IIR filters, Iterative methods, Learning systems, Optimization, Random processes, Adaptive signal processing, Automata theory
Subjects:G Mathematical and Computer Sciences > G700 Artificial Intelligence
H Engineering > H990 Engineering not elsewhere classified
H Engineering > H650 Systems Engineering
Divisions:College of Science > School of Engineering
ID Code:11674
Deposited On:04 Oct 2013 08:45

Repository Staff Only: item control page