Adaptive filter design for active noise cancellation using recurrent type-2 fuzzy brain emotional learning neural network


This article aims to develop a more efficient adaptive filter for the active noise cancellation (ANC). A novel recurrent interval type-2 fuzzy brain emotional learning filter (RT2BELF) is proposed for achieving favourable filtering performance. The ANC is a method to eliminate noise by creating an anti-noise signal which has the same magnitude but opposite phase with the unwanted noise. In order to adapt to the change of the noise, the parameters for the RIT2BELF are online updated based on the adaptive laws, which are derived by the steepest descent gradient approach. The performance of the proposed ANC design method is successfully demonstrated based on numerical simulation results in the real signals. Finally, the superiority of the proposed method is confirmed by the results comparison with some noise cancellation methods.

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  1. 1.

    Kuo SM, Morgan DR (1999) Active noise control: a tutorial review. Proc IEEE 87(6):943–973

    Article  Google Scholar 

  2. 2.

    Paul L (1936) Process of silencing sound oscillations. Google Patents

  3. 3.

    Lin C-M, Yang M-S, Chao F, Hu X-M, Zhang J (2016) Adaptive filter design using type-2 fuzzy cerebellar model articulation controller. IEEE Trans Neural Netw Learn Syst 27(10):2084–2094

    MathSciNet  Article  Google Scholar 

  4. 4.

    Zhang Y, Wen J, Han Y (2018) Adaptive learning based active noise cancellation. In: Proceedings of the 3rd international conference on multimedia and image processing, 2018, pp 41–45

  5. 5.

    Tsao Y, Chu H-C, Fang S-H, Lee J, Lin C-M (2018) Adaptive noise cancellation using deep cerebellar model articulation controller. IEEE Access 6:37395–37402

    Article  Google Scholar 

  6. 6.

    Zhao J, Lin C-M (2019) Wavelet-TSK-type fuzzy cerebellar model neural network for uncertain nonlinear systems. IEEE Trans Fuzzy Syst 27(3):549–558

    Article  Google Scholar 

  7. 7.

    Ho C-Y, Shyu K-K, Chang C-Y, Kuo SM (2018) Integrated active noise control for open-fit hearing aids with customized filter. Appl Acoust 137:1–8

    Article  Google Scholar 

  8. 8.

    Lin C-M, Le T-L (2017) WCMAC-based control system design for nonlinear systems using PSO. J Intell Fuzzy Syst 33(2):807–818

    Article  Google Scholar 

  9. 9.

    Lin C-M, Huynh T-T, Le T-L (2018) Adaptive TOPSIS fuzzy CMAC back-stepping control system design for nonlinear systems. Soft Comput.

    Article  Google Scholar 

  10. 10.

    Wang J-G, Tai S-C, Lin C-J (2018) The application of an interactively recurrent self-evolving fuzzy CMAC classifier on face detection in color images. Neural Comput Appl 29(6):201–213

    Article  Google Scholar 

  11. 11.

    Lin C-M, Huynh T-T (2018) Function-link fuzzy cerebellar model articulation controller design for nonlinear chaotic systems using TOPSIS multiple attribute decision-making method. Int J Fuzzy Syst 20(6):1839–1856

    MathSciNet  Article  Google Scholar 

  12. 12.

    Lin C-M, Le T-L (2017) PSO-self-organizing interval type-2 fuzzy neural network for antilock braking systems. Int J Fuzzy Syst 19(5):1362–1374

    MathSciNet  Article  Google Scholar 

  13. 13.

    Lin C-M, Le T-L, Huynh T-T (2018) Self-evolving function-link interval type-2 fuzzy neural network for nonlinear system identification and control. Neurocomputing 275:2239–2250

    Article  Google Scholar 

  14. 14.

    Eyoh I, John R, De Maere G (2017) Interval type-2 intuitionistic fuzzy logic for regression problems. IEEE Trans Fuzzy Syst 26(4):2396–2408

    Article  Google Scholar 

  15. 15.

    Zirkohi MM, Lin T-C (2015) Interval type-2 fuzzy-neural network indirect adaptive sliding mode control for an active suspension system. Nonlinear Dyn 79(1):513–526

    Article  Google Scholar 

  16. 16.

    Kalaam RN, Muyeen S, Al-Durra A, Hasanien HM, Al-Wahedi K (2017) Optimisation of controller parameters for grid-tied photovoltaic system at faulty network using artificial neural network-based cuckoo search algorithm. IET Renew Power Gener 11(12):1517–1526

    Article  Google Scholar 

  17. 17.

    Chittora P, Singh A, Singh M (2018) Chebyshev functional expansion based artificial neural network controller for shunt compensation. IEEE Trans Ind Inf 14(9):3792–3800

    Article  Google Scholar 

  18. 18.

    Sun Y, Li S, Lin B, Fu X, Ramezani M, Jaithwa I (2017) Artificial neural network for control and grid integration of residential solar photovoltaic systems. IEEE Trans Sustain Energy 8:1484–1495

    Article  Google Scholar 

  19. 19.

    Kumar A, Singh R, Mahodi CS, Sahoo SK (2017) Control of induction motor using artificial neural network. In: Artificial intelligence and evolutionary computations in engineering systems, pp 791–804

  20. 20.

    Zhou Q, Chao F, Lin C-M (2018) A functional-link-based fuzzy brain emotional learning network for breast tumor classification and chaotic system synchronization. Int J Fuzzy Syst 20(2):349–365

    MathSciNet  Article  Google Scholar 

  21. 21.

    Milad HS, Farooq U, El-Hawary ME, Asad MU (2017) Neo-fuzzy integrated adaptive decayed brain emotional learning network for online time series prediction. IEEE Access 5:1037–1049

    Article  Google Scholar 

  22. 22.

    Jafari M, Fehr R, Carrillo LRG, Xu H (2017) Brain emotional learning-based intelligent tracking control for unmanned aircraft systems with uncertain system dynamics and disturbance. In: 2017 International conference on unmanned aircraft systems (ICUAS), pp 1470–1475

  23. 23.

    Khorashadizadeh S, Mahdian M (2016) Voltage tracking control of DC–DC boost converter using brain emotional learning. In: 2016 4th international conference on control, instrumentation, and automation (ICCIA), pp 268–272

  24. 24.

    Hsu C-F, Su C-T, Lee T-T (2016) Chaos synchronization using brain-emotional-learning-based fuzzy control. In: 2016 Joint 8th international conference on soft computing and intelligent systems (SCIS) and 17th international symposium on advanced intelligent systems, pp 811–816

  25. 25.

    Lin C-M, Chung C-C (2015) Fuzzy brain emotional learning control system design for nonlinear systems. Int J Fuzzy Syst 17(2):117–128

    MathSciNet  Article  Google Scholar 

  26. 26.

    LeDoux J (1991) Emotion and the limbic system concept. Concepts Neurosci 2:169–199

    Google Scholar 

  27. 27.

    Le T-L, Lin C-M, Huynh T-T (2018) Self-evolving type-2 fuzzy brain emotional learning control design for chaotic systems using PSO. Appl Soft Comput 73:418–433

    Article  Google Scholar 

  28. 28.

    Melin P, Castillo O (2014) A review on type-2 fuzzy logic applications in clustering, classification and pattern recognition. Appl Soft Comput 21:568–577

    Article  Google Scholar 

  29. 29.

    Castillo O, Martínez-Marroquín R, Melin P, Valdez F, Soria J (2012) Comparative study of bio-inspired algorithms applied to the optimization of type-1 and type-2 fuzzy controllers for an autonomous mobile robot. Inf Sci 192:19–38

    Article  Google Scholar 

  30. 30.

    Oh S-K, Jang H-J, Pedrycz W (2011) A comparative experimental study of type-1/type-2 fuzzy cascade controller based on genetic algorithms and particle swarm optimization. Expert Syst Appl 38(9):11217–11229

    Article  Google Scholar 

  31. 31.

    Mendel JM (2010) A quantitative comparison of interval type-2 and type-1 fuzzy logic systems: first results. In: 2010 IEEE international conference on fuzzy systems (FUZZ), pp 1–8

  32. 32.

    Zadeh LA (1975) The concept of a linguistic variable and its application to approximate reasoning—I. Inf Sci 8(3):199–249

    MathSciNet  Article  Google Scholar 

  33. 33.

    Liang Q, Mendel JM (2000) Interval type-2 fuzzy logic systems: theory and design. IEEE Trans Fuzzy Syst 8(5):535–550

    Article  Google Scholar 

  34. 34.

    Li H, Wang J, Wu L, Lam H-K, Gao Y (2018) Optimal guaranteed cost sliding-mode control of interval type-2 fuzzy time-delay systems. IEEE Trans Fuzzy Syst 26(1):246–257

    Article  Google Scholar 

  35. 35.

    Pratama M, Zhang G, Er MJ, Anavatti S (2017) An incremental type-2 meta-cognitive extreme learning machine. IEEE Trans Cybern 47(2):339–353

    Google Scholar 

  36. 36.

    Sabahi K, Ghaemi S, Pezeshki S (2017) Gain scheduling technique using MIMO type-2 fuzzy logic system for LFC in restructure power system. Int J Fuzzy Syst 19(5):1464–1478

    MathSciNet  Article  Google Scholar 

  37. 37.

    Kim C-J, Chwa D (2015) Obstacle avoidance method for wheeled mobile robots using interval type-2 fuzzy neural network. IEEE Trans Fuzzy Syst 23(3):677–687

    Article  Google Scholar 

  38. 38.

    Wu T, Liu X, Liu F (2018) An interval type-2 fuzzy TOPSIS model for large scale group decision making problems with social network information. Inf Sci 432:392–410

    MathSciNet  Article  Google Scholar 

  39. 39.

    Qin J, Liu X, Pedrycz W (2017) An extended TODIM multi-criteria group decision making method for green supplier selection in interval type-2 fuzzy environment. Eur J Oper Res 258(2):626–638

    MathSciNet  Article  Google Scholar 

  40. 40.

    Mendel JM (2001) Uncertain rule-based fuzzy logic systems: introduction and new directions. Prentice Hall, PTR Upper Saddle River

    Google Scholar 

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The authors appreciate the financial support in part from the Ministry of Science and Technology of Republic of China under Grant MOST 106-2221-E-155-002-MY3.

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Correspondence to Chih-Min Lin.

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Le, T., Huynh, T. & Lin, C. Adaptive filter design for active noise cancellation using recurrent type-2 fuzzy brain emotional learning neural network. Neural Comput & Applic 32, 8725–8734 (2020).

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  • Active noise cancellation
  • Type-2 fuzzy system
  • Brain emotional learning network
  • Recurrent neural network