An adaptive evolutionary algorithm for Volterra system identification

In this paper a real-coded genetic algorithm (GA) for Volterra system identification is presented. The adaptive GA method suggested here addresses the problem of determining the proper Volterra candidates which closely model the identified nonlinear system. A variable length GA chromosomes will encode the coefficients of the selected candidates. A number of candidates with the highest correlation with the output are selected to undergo the first evolution ''era''. The candidates with the least significant contribution in the error reduction process are removed during evolution. Then the next set of candidates are applied into the next era until a solution is found. The proposed GA method handles the issues of detecting the proper Volterra candidates and calculating the associated coefficients as a nonseparable process. The proposed algorithms has produced excellent results in modeling different nonlinear systems.

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