A new design method based on firefly algorithm for IIR system identification problem

被引：26

作者：

Upadhyay P. ^{[1
]}

Kar R. ^{[1
]}

Mandal D. ^{[1
]}

Ghoshal S.P. ^{[2
]}

机构：

[1] Department of Electronics and Communication Engineering, National Institute of Technology, Durgapur, West Bengal

[2] Department of Electrical Engineering, National Institute of Technology, Durgapur, West Bengal

来源：

Journal of King Saud University - Engineering Sciences | 2016年 / 28卷 / 02期

关键词：

Coefficient convergence; Evolutionary Optimization Techniques; FFA; IIR Adaptive Filter; Mean Square Error;

D O I：

10.1016/j.jksues.2014.03.001

中图分类号：

学科分类号：

摘要：

In this paper a population based evolutionary optimization methodology called firefly algorithm (FFA) is applied for the optimization of system coefficients of the infinite impulse response (IIR) system identification problem. FFA is inspired by the flash pattern and characteristics of fireflies. In FFA technique, behaviour of flashing firefly towards its competent mate is structured. In this algorithm attractiveness depends on brightness of light and a bright firefly feels more attraction for the brighter one. For this optimization problem, brightness varies inversely proportional to the error fitness value, so the position of the brightest firefly gives the optimum result corresponding to the least error fitness in multidimensional search space. Incorporation of different control parameters in basic movement equation results in balancing of exploration and exploitation of search space. The proposed FFA based system identification approach has alleviated from inherent drawbacks of premature convergence and stagnation, unlike genetic algorithm (GA), particle swarm optimization (PSO) and differential evolution (DE). The simulation results obtained for some well known benchmark examples justify the efficacy of the proposed system identification approach using FFA over GA, PSO and DE in terms of convergence speed, identifying plant coefficients and mean square error (MSE) fitness values produced for both same order and reduced order models of adaptive IIR filters. © 2014

引用

页码：174 / 198

页数：24

共 38 条

[1]

Berro A., Larabi Marie-Sainte S., Ruiz-Gazen A., Genetic algorithms and particle swarm optimization for exploratory projection pursuit, Ann. Math. Artif. Intell., 60, pp. 153-178, (2010)

[2]

Biswal B., Dash P.K., Panigrahi B.K., Power quality disturbance classification using fuzzy C-means algorithm and adaptive particle swarm optimization, IEEE Trans. Industr. Electron., 56, 1, pp. 212-220, (2009)

[3]

Chen S., IIR model identification using batch-recursive adaptive simulated annealing algorithm, Proceeding of the 6th Annual Chinese Automation and Computer Science Conference, pp. 151-156, (2000)

[4]

Chen S., Luk B.L., Digital IIR filter design using particle swarm optimization, Int. J. Model. Ident. Control, 9, 4, pp. 327-335, (2010)

[5]

Dai C., Chen W., Zhu Y., Seeker optimization algorithm for digital IIR filter design, IEEE Trans. Industr. Electron., 57, 5, pp. 1710-1718, (2010)

[6]

Durmus B., Gun A., Parameter identification using particle swarm optimization, 6th International Advanced Technologies Symposium, pp. 188-192, (2011)

[7]

Eberhart R., Shi Y., Comparison between genetic algorithm and particle swarm optimization, Evolutionary Programming VII, pp. 611-616, (1998)

[8]

Fang W., Sun J., Xu W., Analysis of adaptive IIR filter design based on quantum behaved particle swarm optimization, Proceeding of the IEEE World Congress on Intelligent Control and automation, pp. 3396-3400, (2006)

[9]

Fang W., Sun J., Xu W., A new mutated quantum behaved particle swarm optimizer for digital IIR filter, EURASIP J. Adv. Signal Process., (2009)

[10]

Gao H., Diao M., Differential cultural algorithm for digital filters design, Proceeding of the IEEE 2nd International Conference on Computer Modeling an, pp. 459-463, (2010)

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