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Step Size Optimization of LMS Algorithm Using Genetic Algorithm in System Identification

Rajni Rajni, Akash Tayal

Abstract


 

System identification is the art and science of building mathematical models of dynamic systems from observed input-output data. This paper combines Genetic algorithm and LMS algorithm to describe the application of a Genetic Algorithm (GA) to the problem of parameter optimization for an adaptive Finite Impulse Response (FIR) filter. LMS algorithm computes the filter coefficients and GA search the optimal step-size adaptively. Because step-size influences on the stability and performance, so it is necessary to apply method that can control it.. However, the statistical Least Mean Squares method is faster than the genetic algorithm. For this reason we suggest using the genetic algorithm for off-line applications, and the statistical method for on-line adaptation. A hybrid method combining the advantages of both methods is proposed for real world applications. In Genetic algorithm, we have used Roulette wheel Selection, Arithmetic Crossover, Uniform Mutation& .the simulation results of the GA were compared to the traditional fixed step size LMS algorithm


Keywords


Crossover, Genetic Algorithm, LMS, Mutation, Selection, System Identification.

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References


William S. Levine. "The Control Handbook". CRC Press. IEEE Press. 1995

Lennart Ljung.”Perspectives on System Identification” Division of Automatic Control, Linköpings universitet, SE-581 83 Linköping, Sweden

Lip0 Wang, Jagath C. Rajapakse, Kun~hikoF ukushima,Soo-Young Lee, and Xin Yao “The Hybrid Method For Determining An Adaptive Step Size Of The Unknown System Identification Using Genetic Algorithm And LMS Algorithm”. Proceedings of the 9th International Conference on Neural Information Processing (ICONIP'OP), Vol. 2 [4] David Edward Goldberg “Genetic algorithms in search, optimization, and machine learning”. Addison-Wesley Pub. Co., 11-Jan-1989. [5] Crina Grosan and Ajith Abraham “Intelligent Systems” Springerliink Reference library,2011, Volume 17,345-386,DOI: 10.1007/978-3-642-21004-4_14

David E. Goldberg, (1989), Addison-Wesley "Genetic Algorithms in Search, Optimization, and Machine Learning"

S. Sumathi, T. Hamsapriya, P. Surekha “Evolutionary Intelligence: An Introduction to Theory and Applications”Springer [8] B.Widrow and S.D.Stearns, “Adaptive Signal Processing”, Prentice-Hall,. 1985. ▪ O.Macchi, Adaptive Processing:

Z.Banjac, B.Kovacevic, M.Veinovic and M.Milosavljevic ,“Robust least mean square adaptive FIR filter algorithm” IEEE 2001

Oscar Castillo, Oscar Montie1, Roberto Sepulveda, and Patricia Melin. “Application of a Breeder Genetic Algorithm for System Identification in an Adaptive Finite Impulse Response Filter” .IEEE 2001


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