Proceedings of the International Conference
I W S S I P   2005
12th INTERNATIONAL WORKSHOP ON SYSTEMS, SIGNALS & IMAGE PROCESSING

22 - 24 September 2005, Chalkida Greece
 
(from Chapter 1: Invited Addresses and Tutorials on Signals, Coding, Systems and Intelligent Techniques)

 Full Citation and Abstract

0 Title: Computational Intelligence in Feedback Systems
  Author(s): Marios Polycarpou
  Address: Department of Electrical and Computer Engineering, University of Cyprus
  Reference: SSIP-SP1, 2005  pp. 3 - 3
  Abstract/
Summary
Recent technological advances in computing hardware, communications and real-time software have provided the infrastructure for designing intelligent decision and automated control systems. Based on current trends, high performance feedback systems of the future will require greater autonomy in a number of frontiers. First, they need to be able to deal with greater levels of, possibly, time-varying uncertainty. Second, they need to be able to handle uncertainties in the environment, which will allow the feedback system to be more flexible in dealing with unanticipated events such as faults, obstacles and disturbances. Finally, key advances in distributed and mobile computing will allow for exciting possibilities in distributed decision making and control by agent-type systems. This will require feedback systems to operate in distributed environments with cooperative capabilities. One of the key tools for realizing such advances in the performance and autonomy of feedback systems is 'learning.' Feedback systems with learning capabilities can potentially help reduce modeling uncertainty on-line, make feedback systems more 'intelligent' in the presence of uncertainty in the environment, and initiate design methods for cooperative feedback systems in distributed environments. During the last decade there has been a variety of learning techniques developed for feedback systems, based on structures such as neural networks, fuzzy systems, wavelets, etc. The goal of this presentation is to provide a unifying framework for designing and analyzing feedback systems with learning capabilities. Various on-line approximation techniques and learning algorithms will be presented and illustrated, and directions for future research will be discussed.
 

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