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Artificial Intelligence Review

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01-08-2016

IIR model identification using a modified inclined planes system optimization algorithm

Authors: Ali Mohammadi, Seyed Hamid Zahiri

Published in: Artificial Intelligence Review | Issue 2/2017

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Abstract

Inclined planes system optimization (IPO) is a new optimization algorithm inspired by the sliding motion dynamic along a frictionless inclined surface. In this paper, with the aim of create a powerful trade-off between the concepts of exploitation and exploration, and rectify the complexity of their structural parameters in the standard IPO, a modified version of IPO (called MIPO) is introduced as an efficient optimization algorithm for digital infinite-impulse-response (IIR) filters model identification. The IIR model identification is a complex and practical challenging problem due to multimodal error surface entanglement that many researches have been reported for it. In this work, MIPO utilizes an appropriate mechanism based on the executive steps of algorithm with the constant damp factors. To do this, unknown filter parameters are considered as a vector to be optimized. In implementation, at first, to demonstrate the effectiveness of the proposed method, 10 well-known benchmark functions have been considered for evaluating and testing. In addition, statistical analysis on the powerfulness, efficiency and applicability of the MIPO algorithm are presented. Obtained results in compared to some other popular methods, confirm the efficiency of the MIPO algorithm that makes the best optimal solutions and has a better performance and acceptable solutions.

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Avalos JG, Velazquez J, Sanchez JC (2011) Applications of adaptive filtering. INTECH Open Access Publisher. www.intechopen.com

Chen S (2000) IIR model identification using batch-recursive adaptive simulated annealing algorithm In: Proceeding of the 6th annual Chinese automation and computer science conference, UK, pp. 151–156

Chen S, Istepanian R, Luk BL (2001) Digital IIR filter design using adaptive simulated annealing. Digit Signal Process 11(3):241–251CrossRef

Dai C, Chen W, Zhu Y (2010) Seeker optimization algorithm for digital IIR filter design. IEEE Trans Ind Electron 57(5):1710–1718. doi:10.1109/TIE.2009.2031194 CrossRef

Fang W, Sun J, Xu W (2010) A new mutated quantum-behaved particle swarm optimizer for digital IIR filter design. EURASIP J Adv Signal Process 2009(1):367465MATH

Hegde V, Pai S, Jenkins WK, Wilborn TB (2000) Genetic algorithms for adaptive phase equalization of minimum phase SAW filters. In: Conference record of the thirty-fourth asilomar conference on signals, systems and computers (Cat. No. 00CH37154), 2. doi:10.1109/ACSSC.2000.911269

Inkaya ET (2011) A novel and efficient algorithm for adaptive filtering: artificial bee colony algorithm. Turk J Electr Eng Comput Sci 19(1):175–190. doi:10.3906/elk-0912-344

Ioannou P, Fidan B (2006) Adaptive control tutorial (Advances in design and control). Society for Industrial and Applied Mathematics (SIAM), Philadelphia

Kalinli A, Karaboga N (2005a) A new method for adaptive IIR filter design based on tabu search algorithm. AEU Int J Electron Commun 59(2):111–117. doi:10.1016/j.aeue.2004.11.003 CrossRefMATH

Kalinli A, Karaboga N (2005) Artificial immune algorithm for IIR filter design. Eng Appl Artif Intell. doi:10.1016/j.engappai.2005.03.009 MATH

Karaboga N (2005) Digital IIR filter design using differential evolution algorithm. EURASIP J Appl Signal Process 2005:1269–1276CrossRefMATH

Karaboga N (2009) A new design method based on artificial bee colony algorithm for digital IIR filters. J Frankl Inst 346(4):328–348. doi:10.1016/j.jfranklin.2008.11.003 MathSciNetCrossRefMATH

Karaboga N, Kalinli A, Karaboga D (2004) Designing digital IIR filters using ant colony optimisation algorithm. Eng Appl Artif Intell 17(3):301–309. doi:10.1016/j.engappai.2004.02.009 CrossRefMATH

Krusienski DJ (2004) Enhanced structured stochastic global optimization algorithms for IIR and nonlinear adaptive filtering. PhD Thesis, Department of Electrical Engineering, The Pennsylvania State University, University Park, PA

Krusienski DJ, Jenkins WK (2004) A particle swarm optimization-least mean squares algorithm for adaptive filtering. In: Conference record of the thirty-eighth asilomar conference on signals, systems and computers, vol 1, pp 241–245. doi:10.1109/ACSSC.2004.1399128

Krusienski D J, Jenkins WK (2004) Particle swarm optimization for adaptive IIR filter structures. In: Evolutionary computation. CEC2004. Congress on, vol 1, pp 965–970. IEEE

Krusienski DJ, Jenkins WK (2005) Design and performance of adaptive systems based on structured stochastic optimization strategies. IEEE Circuits Syst Mag. doi:10.1109/MCAS.2005.1405897

Ling SH, Iu H, Leung FHF, Chan KY (2008) Improved hybrid particle swarm optimized wavelet neural network. IEEE Trans Ind Electron. doi:10.1109/TIE.2008.922599

Luitel B, Venayagamoorthy GK (2010) Particle swarm optimization with quantum infusion for system identification. Eng Appl Artif Intell 23(5):635–649. doi:10.1016/j.engappai.2010.01.022 CrossRef

Luukko J, Rauma K (2008) Open-loop adaptive filter for power electronics applications. IEEE Trans Ind Electron. doi:10.1109/TIE.2007.909738

Majhi B, Panda G (2009) Identification of IIR systems using comprehensive learning particle swarm optimisation. Int J Power Energy Convers 1(1):105–124. doi:10.1504/IJPEC.2009.023478 CrossRef

Montiel O, Castillo O, Melin P, Sepulveda R (2003) The evolutionary learning rule for system identification. Appl Soft Comput 3(4):343–352. doi:10.1016/j.asoc.2003.05.005 CrossRef

Mostajabi T, Poshtan J (2011) Control and system identification via swarm and evolutionary algorithms. Int J Sci Eng Res 2(10):1–6

Mostajabi T, Poshtan J, Mostajabi Z (2013) IIR model identification via evolutionary algorithms. Artif Intell Rev 44(1):87–101. doi:10.1007/s10462-013-9403-1 CrossRef

Mozaffari MH, Abdy H, Zahiri S-H (2016) IPO: an inclined planes system optimization algorithm. Comput Inform 35(1):222–240MathSciNet

Netto SL, Diniz PSR, Agathoklis P (1995) Adaptive IIR filtering algorithms for system identification: a general framework. IEEE Trans Educ 38(1):54–66. doi:10.1109/13.350221 CrossRef

Ng SC, Leung SH, Chung CY, Luk A, Lau WH (1996) The genetic search approach: a new learning algorithm for adaptive IIR filtering. IEEE Signal Process Mag 13(6):38–46. doi:10.1109/79.543974 CrossRef

Panda G, Pradhan PM, Majhi B (2011) IIR system identification using cat swarm optimization. Expert Syst Appl 38(10):12671–12683. doi:10.1016/j.eswa.2011.04.054 CrossRef

Rashedi E, Nezamabadi-Pour H, Saryazdi S (2011) Filter modeling using gravitational search algorithm. Eng Appl Artif Intell 24(1):117–122. doi:10.1016/j.engappai.2010.05.007 CrossRefMATH

Sharifi M, Mojallali H (2014) Design of IIR digital filter using modified chaotic orthogonal imperialist competitive algorithm (research note). Int J Eng Trans A Basics. doi:10.5829/idosi.ije.2014.27.07a.04

Saha SK, Kar R, Mandal D, Ghoshal SP (2014) Harmony search algorithm for infinite impulse response system identification. Comput Electr Eng 40(4):1265–1285. doi:10.1016/j.compeleceng.2013.12.016 CrossRef

Shynk JJ (1989) Adaptive IIR filtering. ASSP Mag IEEE 6(2):4–21CrossRef

Singh R, Verma HK (2014) Teaching–learning-based optimization algorithm for parameter identification in the design of IIR filters. J Inst Eng (India) Ser B 94(4):285–294. doi:10.1007/s40031-013-0063-y CrossRef

Widrow B, Stearns SD (1985) Adaptive signal processing. adaptive signal processing, vol 78. http://adsabs.harvard.edu/abs/1985ph...book.....W

Wilson PB, Macleod MD (1993) Low implementation cost IIR digital filter design using genetic algorithms. In: IEE/IEEE workshop on natural algorithms in signal processing, vol 1, pp 1–4

Yao X, Liu Y, Lin G (1999) Evolutionary programming made faster. IEEE Trans Evolut Comput 3(2):82–102. doi:10.1109/4235.771163 CrossRef

Title: IIR model identification using a modified inclined planes system optimization algorithm
Authors: Ali Mohammadi
Seyed Hamid Zahiri
Publication date: 01-08-2016
Publisher: Springer Netherlands
Published in: Artificial Intelligence Review / Issue 2/2017
Print ISSN: 0269-2821
Electronic ISSN: 1573-7462
DOI: https://doi.org/10.1007/s10462-016-9500-z

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