Adaptive infinite impulse response system identification using modified-interior search algorithm with Lèvy flight.

In this paper, a new meta-heuristic optimization technique, called interior search algorithm (ISA) with Lèvy flight is proposed and applied to determine the optimal parameters of an unknown infinite impulse response (IIR) system for the system identification problem. ISA is based on aesthetics, which is commonly used in interior design and decoration processes. In ISA, composition phase and mirror phase are applied for addressing the nonlinear and multimodal system identification problems. System identification using modified-ISA (M-ISA) based method involves faster convergence, single parameter tuning and does not require derivative information because it uses a stochastic random search using the concepts of Lèvy flight. A proper tuning of control parameter has been performed in order to achieve a balance between intensification and diversification phases. In order to evaluate the performance of the proposed method, mean square error (MSE), computation time and percentage improvement are considered as the performance measure. To validate the performance of M-ISA based method, simulations has been carried out for three benchmarked IIR systems using same order and reduced order system. Genetic algorithm (GA), particle swarm optimization (PSO), cat swarm optimization (CSO), cuckoo search algorithm (CSA), differential evolution using wavelet mutation (DEWM), firefly algorithm (FFA), craziness based particle swarm optimization (CRPSO), harmony search (HS) algorithm, opposition based harmony search (OHS) algorithm, hybrid particle swarm optimization-gravitational search algorithm (HPSO-GSA) and ISA are also used to model the same examples and simulation results are compared. Obtained results confirm the efficiency of the proposed method.

[1]  Anil Kumar,et al.  Hybrid method for designing digital FIR filters based on fractional derivative constraints. , 2015, ISA transactions.

[2]  Sakti Prasad Ghoshal,et al.  IIR system identification using differential evolution with wavelet mutation , 2014 .

[3]  Amir Hossein Gandomi,et al.  Engineering optimization using interior search algorithm , 2014, 2014 IEEE Symposium on Swarm Intelligence.

[4]  Dervis Karaboga,et al.  Artificial bee colony algorithm , 2010, Scholarpedia.

[5]  Hossein Nezamabadi-pour,et al.  GSA: A Gravitational Search Algorithm , 2009, Inf. Sci..

[6]  Sakti Prasad Ghoshal,et al.  A new design method using opposition-based BAT algorithm for IIR system identification problem , 2013, Int. J. Bio Inspired Comput..

[7]  Tarun Kumar Rawat,et al.  Design of optimal digital FIR filters using evolutionary and swarm optimization techniques , 2016 .

[8]  Sakti Prasad Ghoshal,et al.  Harmony search algorithm for infinite impulse response system identification , 2014, Comput. Electr. Eng..

[9]  Hossein Nezamabadi-pour,et al.  Filter modeling using gravitational search algorithm , 2011, Eng. Appl. Artif. Intell..

[10]  Nurhan Karaboga,et al.  Artificial immune algorithm for IIR filter design , 2005, Eng. Appl. Artif. Intell..

[11]  Ganapati Panda,et al.  IIR system identification using cat swarm optimization , 2011, Expert Syst. Appl..

[12]  Jun Sun,et al.  Analysis of Adaptive IIR Filter Design Based on Quantum-behaved Particle Swarm Optimization , 2006, 2006 6th World Congress on Intelligent Control and Automation.

[13]  B. Durmuş,et al.  Parameter Identification using Particle Swarm Optimization , 2011 .

[14]  Nurhan Karaboga,et al.  Digital IIR Filter Design Using Differential Evolution Algorithm , 2005, EURASIP J. Adv. Signal Process..

[15]  Zong Woo Geem,et al.  A New Heuristic Optimization Algorithm: Harmony Search , 2001, Simul..

[16]  Sanjit K. Mitra,et al.  Digital Signal Processing: A Computer-Based Approach , 1997 .

[17]  Yan Wang,et al.  A new design method for adaptive IIR system identification using hybrid particle swarm optimization and gravitational search algorithm , 2015 .

[18]  Nithin V. George,et al.  On a cuckoo search optimization approach towards feedback system identification , 2014, Digit. Signal Process..

[19]  Chaohua Dai,et al.  Seeker Optimization Algorithm for Digital IIR Filter Design , 2010, IEEE Transactions on Industrial Electronics.

[20]  Ali H. Sayed,et al.  Fundamentals Of Adaptive Filtering , 2003 .

[21]  Nikolaus Hansen,et al.  Completely Derandomized Self-Adaptation in Evolution Strategies , 2001, Evolutionary Computation.

[22]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[23]  Sakti Prasad Ghoshal,et al.  A new design method based on firefly algorithm for IIR system identification problem , 2016 .

[24]  S. Haykin,et al.  Adaptive Filter Theory , 1986 .

[25]  Bing Lam Luk,et al.  Digital IIR filter design using particle swarm optimisation , 2010, Int. J. Model. Identif. Control..

[26]  Tarun Kumar Rawat,et al.  Optimal design of FIR fractional order differentiator using cuckoo search algorithm , 2015, Expert Syst. Appl..

[27]  P. N. Suganthan,et al.  Differential Evolution: A Survey of the State-of-the-Art , 2011, IEEE Transactions on Evolutionary Computation.

[28]  Xin-She Yang,et al.  Nature-Inspired Metaheuristic Algorithms , 2008 .

[29]  Durbadal Mandal,et al.  A novel design method for optimal IIR system identification using opposition based harmony search algorithm , 2014, J. Frankl. Inst..

[30]  Nurhan Karaboga,et al.  A new design method based on artificial bee colony algorithm for digital IIR filters , 2009, J. Frankl. Inst..

[31]  Ganesh K. Venayagamoorthy,et al.  Particle swarm optimization with quantum infusion for system identification , 2010, Eng. Appl. Artif. Intell..

[32]  Sakti Prasad Ghoshal,et al.  Cat Swarm Optimization algorithm for optimal linear phase FIR filter design. , 2013, ISA transactions.

[33]  Bernard Widrow,et al.  Adaptive Signal Processing , 1985 .

[34]  Dervis Karaboga,et al.  Designing digital IIR filters using ant colony optimisation algorithm , 2004, Eng. Appl. Artif. Intell..

[35]  Dean J. Krusienski,et al.  Particle swarm optimization for adaptive IIR filter structures , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[36]  Xin-She Yang,et al.  Cuckoo Search via Lévy flights , 2009, 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC).

[37]  Tarun Kumar Rawat,et al.  Optimal fractional delay-IIR filter design using cuckoo search algorithm. , 2015, ISA transactions.

[38]  A. Gandomi.  Interior search algorithm (ISA): a novel approach for global optimization. , 2014, ISA transactions.

[39]  Tarun Kumar Rawat,et al.  Optimal Design of 2D FIR Filters with Quadrantally Symmetric Properties Using Fractional Derivative Constraints , 2016, Circuits Syst. Signal Process..

[40]  Sakti Prasad Ghoshal,et al.  Craziness based particle swarm optimization algorithm for IIR system identification problem , 2014 .

[41]  Ganapati Panda,et al.  Efficient scheme of pole-zero system identification using Particle Swarm Optimization technique , 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).

[42]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[43]  Paulo S. R. Diniz,et al.  Adaptive Filtering: Algorithms and Practical Implementation , 1997 .

[44]  Wenbo Xu,et al.  A New Mutated Quantum-Behaved Particle Swarm Optimizer for Digital IIR Filter Design , 2010, EURASIP J. Adv. Signal Process..

[45]  Tarun Kumar Rawat,et al.  Optimal design of FIR high pass filter based on L1 error approximation using real coded genetic algorithm , 2015 .