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Combined algorithm for time-varying system based on improved genetic algorithm and EWRLS algorithm

Yuncan Xue, Qiwen Yang, Jixin Qian

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Abstract

A combined algorithm, based on the modified genetic algorithm and EWRLS algorithm, is presented in this paper. A modified genetic algorithm with dyadic mutation operator is also presented to enhance the response speed of genetic algorithm. The selection criteria of the switching threshold between the GA and EWRLS algorithm is also given by using robust minmax estimation method. This combined algorithm solves the tracking problem of time-varying system with fast parameter changes, which is very difficult to the RLS algorithm. It is not sensitive to the noise. Its good performance is verified by simulation studies.

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What this paper is about

A combined algorithm, based on the modified genetic algorithm and EWRLS algorithm, is presented in this paper. A modified genetic algorithm with dyadic mutation operator is also presented to enhance the response speed of genetic algorithm. The selection criteria of the switching threshold between the GA and EWRLS algorithm is also given by using robust minmax estimation method. This combined algorithm solves the tracking problem of time-varying system with fast parameter changes, which is very difficult to the RLS algorithm. It is not sensitive to the noise. Its good performance is verified by simulation studies.

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Available abstract

A combined algorithm, based on the modified genetic algorithm and EWRLS algorithm, is presented in this paper. A modified genetic algorithm with dyadic mutation operator is also presented to enhance the response speed of genetic algorithm. The selection criteria of the switching threshold between the GA and EWRLS algorithm is also given by using robust minmax estimation method. This combined algorithm solves the tracking problem of time-varying system with fast parameter changes, which is very difficult to the RLS algorithm. It is not sensitive to the noise. Its good performance is verified by simulation studies.

Key concepts: Algorithm, Population-based incremental learning, Genetic algorithm, Computer science, Algorithm design, Selection (genetic algorithm), Cultural algorithm, Minimax

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