Hybrid genetic and Nelder-Mead algorithms for identification of time delays

Abstract The identification of time delay in the linear plant is one of the important identification tasks. Most of the conventional identification techniques, such as those based on least mean-squares, are essentially gradient-guided local search techniques and they require a smooth search space or a differentiable performance index. New possibility in this field is opened by an application of the hybrid genetic algorithms. The center of mass simplex crossover (CMSPX) algorithm is proposed in the paper. This algorithm is compared with other classical and genetic methods, by using the efficiency of looking for time delay and time of computation as a performance measure. The obtained results suggest that the proposed method performs well in estimating the model parameters.

[1]  Andrey V. Savkin,et al.  Robust state estimation for a class of uncertain time-delay systems , 2002, Syst. Control. Lett..

[2]  Alan Olinsky,et al.  A genetic algorithm approach to nonlinear least squares estimation , 2004 .

[3]  Samir Shaltaf.  Neural-Network-Based Time-Delay Estimation , 2004, EURASIP J. Adv. Signal Process..

[4]  Helio J. C. Barbosa,et al.  A GA-Simplex Hybrid Algorithm for Global Minimization of Molecular Potential Energy Functions , 2005, Ann. Oper. Res..

[5]  D. Westwick,et al.  Separable Least Squares Identification of Nonlinear Hammerstein Models: Application to Stretch Reflex Dynamics , 2001, Annals of Biomedical Engineering.

[6]  Patrick Siarry,et al.  Genetic and Nelder-Mead algorithms hybridized for a more accurate global optimization of continuous multiminima functions , 2003, Eur. J. Oper. Res..

[7]  John A. Nelder,et al.  A Simplex Method for Function Minimization , 1965, Comput. J..

[8]  Janusz P. Paplinski.  An Evolutionary Algorithm for Identification of Non-Stationary Linear Plants With Time Delay , 2004, ICINCO.

[9]  X. Chen,et al.  Global optimization methods for time delay estimation , 2004, Fifth World Congress on Intelligent Control and Automation (IEEE Cat. No.04EX788).

[10]  Kiyoshi Wada,et al.  A global nonlinear instrumental variable method for identification of continuous-time systems with u , 2005 .

[11]  Bor-Sen Chen,et al.  A global estimation for multichannel time-delay and signal parameters via genetic algorithm , 2001, Signal Process..

[12]  M. Lovera,et al.  Identification of non-linear parametrically varying models using separable least squares , 2004 .

[13]  Shu-Kai S. Fan,et al.  A genetic algorithm and a particle swarm optimizer hybridized with Nelder-Mead simplex search , 2006, Comput. Ind. Eng..

[14]  S. Bjorklund,et al.  A review of time-delay estimation techniques , 2003, 42nd IEEE International Conference on Decision and Control (IEEE Cat. No.03CH37475).

[15]  Vivek S. Borkar,et al.  Stochastic approximation algorithms: Overview and recent trends , 1999 .

[17]  G. R. Hext,et al.  Sequential Application of Simplex Designs in Optimisation and Evolutionary Operation , 1962 .