Global search of adaptive IIR filter error surfaces using stochastic learning automata

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Abstract
The multimodal nature of adaptive IIR filter error surfaces limits the use of gradient search adaptive algorithms. Recently an intelligent learning approach was suggested by the authors to tackle the problem. This paper describes further research results and shows that stochastic learning automata are capable of locating the global minimum under different conditions. Computer simulation results for a system identification application are presented to illustrate that it is possible to achieve global convergence irrespective of insufficient filter order or input colouration or both. Stability during adaptation is also maintained.

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