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Selfish herd optimization algorithm based on chaotic strategy for adaptive IIR system identification problem

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Abstract

The design method of adaptive infinite impulse response (IIR) filter is a challenging problem. Its design principle is to determine the filter parameters by the iteration process of the adaptive algorithm, which is to obtain an optimal model for unknown plant based on minimizing mean square error (MSE). However, many adaptive algorithms cannot adjust the parameters of IIR filter to the minimum MSE. Therefore, a more efficient adaptive optimization algorithm is required to adjust the parameters of IIR filter. In this paper, we propose a selfish herd optimization algorithm based on chaotic strategy (CSHO) and apply it to solving IIR system identification problem. In CSHO, we add a chaotic search strategy, which is a better local optimization strategy. Its function is to search for better candidate solutions around the global optimal solution, which makes the local search of the algorithm more precise and finds out potential global optimal solutions. We use solving IIR system identification problem to verify the effectiveness of CSHO. Ten typical IIR filter models with the same order and reduced order are selected for experiments. The experimental results of CSHO compare with those of bat algorithm (BA), cellular particle swarm optimization and differential evolution (CPSO-DE), firefly algorithm (FFA), hybrid particle swarm optimization and gravitational search algorithm (HPSO-GSA), improved particle swarm optimization (IPSO) and opposition-based harmony search algorithm (OHS), respectively. The experimental results show that CSHO has better optimization accuracy, convergence speed and stability in solving most of the IIR system identification problems. At the same time, it also obtains better optimization parameters and achieves smaller difference between actual output and expected output in test samples.

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References

  • Astrom KJ et al (1995) Adaptive Control. Addison-Wesley, Reading

    MATH  Google Scholar 

  • Cheng S, Li C-W (2011) Fuzzy PDFF-IIR controller for PMSM drive systems. Control Eng Pract 19:828–835

    Article  Google Scholar 

  • Dai C et al (2010) Seeker optimization algorithm for digital IIR filter design. IEEE Trans Ind Electron 57:1710–1718

    Google Scholar 

  • Derrac J, Gracie S, Molina D, Herrera F (2011) A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evol Comput 1:3–18

    Article  Google Scholar 

  • Dimple K, Kotary DK et al (2017) An incremental RLS for distributed parameter estimation of IIR systems present in computing nodes of a wireless sensor network. Procedia Comput Sci 115:699–706

    Article  Google Scholar 

  • Fausto F, Cuevas E et al (2017) A global optimization algorithm inspired in the behavior of selfish herds. Biosystems 160:39–55

    Article  Google Scholar 

  • Gomez C (2018) Recursive identification of IIR systems with multilevel output quantization and lossy memoryless channels with transmission errors. IFAC-PapersOnLine 51:909–914

    Article  Google Scholar 

  • Griffiths EJ, Orponen P (2005) Optimization, block designs and No Free Lunch theorems. Inf Process Lett 94:55–61

    Article  MathSciNet  Google Scholar 

  • Hamilton WD (1971) Geometry to the selfish herd. J Theory Biol 31:295–311

    Article  Google Scholar 

  • Huang C-Y, Lai C-H et al (2018) Inhibition of ERK-Drp1 signaling and mitochondria fragmentation alleviates IGF-IIR-induced mitochondria dysfunction during heart failure. J Mol Cell Cardiol 122:58–68

    Article  Google Scholar 

  • Jiang C, Bompard E (2005) A hybrid method of chaotic particle swarm optimization and linear interior for reactive power optimization. Math Comput Simul 68:57–65

    Article  Google Scholar 

  • Jiang S et al (2015) A new design method for adaptive IIR system identification using hybrid particle swarm optimization and gravitational search algorithm. Nonlinear Dyn 9:2553–2576

    Article  MathSciNet  Google Scholar 

  • Karaboga N et al (2011) A novel and efficient algorithm for adaptive filtering: artificial bee colony algorithm. Turk J Electr Eng Comput Sci 19:175–190

    Google Scholar 

  • Kumar M et al (2016) Bat algorithm: application to adaptive infinite impulse response system identification. Arab J Sci Eng 41:3587–3604

    Article  Google Scholar 

  • Lagos-Eulogio P et al (2017) A new design method for adaptive IIR system identification using hybrid CPSO and DE. Nonlinear Dyn 88:2371–2389

    Article  Google Scholar 

  • Liang W, Lu W, Fan J (2011) The signal detection in TWACS based on IIR filter. Procedia Eng 23:120–124

    Article  Google Scholar 

  • Liu B, Wang L et al (2005) Improved particle swarm optimization combined with chaos. Chaos Solitons Fract 25:1261–1271

    Article  Google Scholar 

  • Mandal S et al (2012) Differential evolution with wavelet mutation in digital FIR filter design. J Optim Theory Appl 155:315–324

    Article  MathSciNet  Google Scholar 

  • Panda G et al (2011) IIR system identification using cat swarm optimization. Expert Syst Appl 38:12671–12683

    Article  Google Scholar 

  • Rashedi E et al (2011) Filter modeling using gravitational search algorithm. Eng Appl Artif Intell 24:117–122

    Article  Google Scholar 

  • Saha S, Mukherjee V (2018) A novel chaos-integrated symbiotic organisms search algorithm for global optimization. Soft Comput 22:3797–3816

    Article  Google Scholar 

  • Saha SK et al (2013) A new design method using opposition-based BAT algorithm for IIR system identification problem. Int J Bio-Inspired Comput 5:99–132

    Article  Google Scholar 

  • Sarangi A et al (2016) An approach to identification of unknown IIR systems using crossover cat swarm optimization. Perspect Sci 8:301–303

    Article  Google Scholar 

  • Scarpiniti M et al (2015) Nonlinear system identification using IIR spline adaptive filters. Sig Process 108:30–35

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Upadhyay P et al (2014) A novel design method for optimal IIR system identification using opposition based harmony search algorithm. J Frankl Inst 351:2454–2488

    Article  MathSciNet  Google Scholar 

  • Upadhyay P et al (2016) A new design method based on firefly algorithm for IIR system identification problem. J King Saud Univ Eng Sci 28:174–198

    Google Scholar 

  • Wang Y, Ding F, Xu L (2018) Some new results of designing an IIR filter with colored noise for signal processing. Digit Signal Proc 72:44–58

    Article  MathSciNet  Google Scholar 

  • Xiang T, Liao X, Wong K (2007) An improved particle swarm optimization algorithm combined with piecewise linear chaotic map. Appl Math Comput 190:1637–1645

    MathSciNet  MATH  Google Scholar 

  • Yan W, Zhang J, Zhang S, Wen P (2018) A novel pipelined neural IIR adaptive filter for speech prediction. Appl Acoust 141:64–70

    Article  Google Scholar 

  • Yao L, Sethares WA et al (1994) Nonlinear parameter estimation via the genetic algorithm. IEEE Trans Signal Process 42:927–935

    Article  Google Scholar 

  • Zhang S, Zhou Y (2018) Grey wolf optimizer with ranking-based mutation operator for IIR model identification. Chin J Electron 27:1071–1079

    Article  Google Scholar 

  • Zou D-X, Deb S, Wang G-G (2018) Solving IIR system identification by a variant of particle swarm optimization. Neural Comput Appl 30:685–698

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank the anonymous reviewers for their valuable comments and suggestions. This paper has been awarded by the National Natural Science Foundation of China (61170035, 61272420, 81674099 and 61502233), the Fundamental Research Fund for the Central Universities (30916011328, 30918015103 and 30918012204), Nanjing Science and Technology Development Plan Project (201805036) and “13th Five-Year” Equipment Field Fund (61403120501).

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Authors and Affiliations

  1. School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, Jiangsu, China

    Ruxin Zhao, Yongli Wang, Peng Hu, Hamed Jelodar, Chi Yuan, YanChao Li, Isma Masood, Mahdi Rabbani, Hao Li & Bo Li

  2. CETC Big Data Research Institute Co., Ltd., Guiyang, 550081, Guizhou, China

    Yongli Wang

  3. School of Mechanical and Automotive Engineering, Yangzhou Polytechnic Institute, Yangzhou, 225127, Jiangsu, China

    Chang Liu

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  1. Ruxin Zhao
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  2. Yongli Wang
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Correspondence to Yongli Wang.

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Zhao, R., Wang, Y., Liu, C. et al. Selfish herd optimization algorithm based on chaotic strategy for adaptive IIR system identification problem. Soft Comput 24, 7637–7684 (2020). https://doi.org/10.1007/s00500-019-04390-9

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