Volterra system identification using adaptive genetic algorithms

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In this paper a floating point 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 leads to the smallest error between the identified nonlinear system and the Volterra model. This is achieved by using variable length GA chromosomes which encode the coefficients of the selected candidates. During the process of evolution the candidates with the least significant contribution in the error reduction process is removed. The proposed GA method detects the proper Volterra candidates and the associated coefficients in one single evolutionary process. The fitness function employed by the algorithm prevents irrelevant candidates from taking part of the final solution. Genetic operators are chosen to suit the floating point representation of the genetic data. As the evolution process improves and the method reaches a near-global solution, a local search is implicitly applied by zooming in the search interval of each gene by adaptively changing the boundaries of those intervals. The proposed algorithms has produced excellent results in modeling different nonlinear systems.

Evolutionary computation
System identification
Volterra systems
Polynomial approximation
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