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IIR model identification using a modified inclined planes system optimization algorithm

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An Erratum to this article was published on 30 August 2016

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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References

  • 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):241–251

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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):367465

    MATH  Google Scholar 

  • 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

    Google Scholar 

  • 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

    Article  MATH  Google Scholar 

  • 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  Google Scholar 

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

    Article  MATH  Google Scholar 

  • 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

    Article  MathSciNet  MATH  Google Scholar 

  • 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

    Article  MATH  Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • 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

    Article  Google Scholar 

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

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

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

    MathSciNet  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  MATH  Google Scholar 

  • 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

    Google Scholar 

  • 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

    Article  Google Scholar 

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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Birjand, Birjand, Iran

    Ali Mohammadi & Seyed Hamid Zahiri

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  1. Ali Mohammadi
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  2. Seyed Hamid Zahiri
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Correspondence to Ali Mohammadi.

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An erratum to this article is available at http://dx.doi.org/10.1007/s10462-016-9512-8.

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Mohammadi, A., Zahiri, S.H. IIR model identification using a modified inclined planes system optimization algorithm. Artif Intell Rev 48, 237–259 (2017). https://doi.org/10.1007/s10462-016-9500-z

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