Proceedings of the International Conference
I W S S I P   2005

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: Evolutionary Lainiotis' Algorithms for System Identification: a Survey
  Author(s): Grigorios N. Beligiannis
  Address: Pattern Recognition Laboratory, Department of Computer Engineering and Informatics, University of Patras, 26500 Rio Patras, Greece
beligian @
  Reference: SSIP-SP1, 2005  pp. 63 - 67
In this contribution, the application of evolutionary Lainiotis' algorithms in realworld adaptive system identification problems is presented. These algorithms combine the effectiveness of adaptive multi model partitioning filters of Lainiotis and Genetic Algorithms' robustness and have been successfully applied to linear and nonlinear system identification problems. Specifically, the a posteriori probability that a specific model, of a bank of the conditional models, is the true model can be used as fitness function for the Genetic Algorithm. In this way, the algorithms identify the true model even in the case where it is not included in the filters' bank. It is clear, that the filter's performance is considerably improved through the evolution of the population of the filters' bank, since the algorithms can search the whole parameter space. The proposed algorithms can be applied to linear and nonlinear data, are not restricted to the Gaussian case, do not require any knowledge of the model switching law, are practically implementable, computationally efficient and applicable to on-line/adaptive operation and exhibit excellent performance as indicated by experimental results. Furthermore, they can be realized in a parallel processing fashion, a fact that makes them amenable to VLSI implementation.
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