Type-2 fuzzy CMAC neural network enhanced by recurrent feedback for active noise cancellation

Document Type : Research Paper

Authors

1 Mechanical Engineering, Lac Hong University, Bien Hoa, Vietnam.

2 No. 10, Huynh Van Nghe, Tran Bien

Abstract

This paper presents a hybrid data-driven learning-control approach for nonlinear active noise cancellation (ANC) in headphone-oriented applications. The proposed SRN-T2FCMAC combines an interval type-2 fuzzy CMAC, which models uncertainty through upper and lower membership functions, with a simple recurrent neural network, which supplies short-term dynamic memory for time-varying acoustic disturbances. The main adaptation strategy updates the consequent weights, membership centers, membership spreads, and recurrent weights by minimizing an instantaneous  residual-noise objective subject to bounded-learning-rate constraints. Simulation is conducted for cubic nonlinear noise,  and real-time DSP6713 verification is conducted in a microphone--loudspeaker headphone-oriented ANC setting. The  evaluation framework uses testing-set MSE, input/output SNR, SNR improvement, residual-noise reduction in dB, and  ablation settings for T2FNN-only, CMAC-only, and complete SRN--T2FCMAC controllers. The simulation and DSP6713  verification results indicate that the proposed hybrid controller achieves effective nonlinear noise suppression and shows  strong potential for real-time headphone-oriented ANC applications. 

Keywords

Main Subjects

References

[1] R. T. Bambang, Adjoint EKF learning in recurrent neural networks for nonlinear active noise control, Applied Soft Computing, 8(4) (2008), 1498-1504. https://doi.org/10.1016/j.asoc.2007.10.017
[2] A. Bayestehtashk, A. Kumar, M. Wurtz, Design of feedback active noise cancellation filter using nested re current neural networks, Proc. Interspeech 2024, (2024), 3270-3274. https://doi.org/10.21437/Interspeech. 2024-1295
[3] Y. J. Cha, A. Mostafavi, S. S. Benipal, DNoiseNet: Deep learning-based feedback active noise control in various noisy environments, Engineering Applications of Artificial Intelligence, 121 (2023), 105971. https://doi.org/ 10.1016/j.engappai.2023.105971
[4] K. M. Das, et al., All-pass filtered x least mean square algorithm for narrowband active noise control, Applied Acoustics, 142 (2018), 1-10. https://doi.org/10.1016/j.apacoust.2018.07.026
[5] Y. Dong, Y. Song, Y. Yuan, et al., Self-triggered model predictive control for interval Type-2 T-S fuzzy systems: A co-design approach, International Journal of Robust and Nonlinear Control, 35(9) (2025), 3471-3483. https: //doi.org/10.1002/rnc.7857
[6] A. Kar, et al., Improved active noise cancellation using variable step-size combined Fx-LMS algorithm, Circuits, Systems, and Signal Processing, 44(1) (2025), 447-461. https://doi.org/10.1007/s00034-024-02848-2
[7] K. Kayisli, Super twisting sliding mode-type 2 fuzzy MPPT control of solar PV system with parameter optimization under variable irradiance conditions, Ain Shams Engineering Journal, 14(1) (2023), 101950. https://doi.org/ 10.1016/j.asej.2022.101950
[8] T. L. Le, Design of intelligent controller using Type-2 fuzzy cerebellar model articulation and 3D member ship functions, International Journal of Fuzzy Systems, 25(3) (2023), 966-979. https://doi.org/10.1007/ s40815-022-01419-4
[9] T. L. Le, T. T. Huynh, C. M. Lin, Adaptive filter design for active noise cancellation using recurrent type-2 fuzzy brain emotional learning neural network, Neural Computing and Applications, 32 (2020), 8725-8734. https: //doi.org/10.1007/s00521-019-04366-8
[10] L. T. Le, L. K. Ngo, Application of combining type 2 fuzzy cmac network and Jordan neural network in nonlinear system control, Iranian Journal of Fuzzy Systems, 22(2) (2025), 25-39. https://doi.org/10.22111/ijfs.2025. 50552.8930
[11] X. Li, K. Shi, J. Cheng, et al., A semi-Markovian model approach to resilient fault-tolerant control of interval Type-2 fuzzy systems with stochastic actuator failures and its applications, International Journal of Robust and Nonlinear Control, 35(6) (2025), 2399-2424. https://doi.org/10.1002/rnc.7805
[12] F. Liu, X. Zhao, Z. Zhu, et al., Dual-microphone active noise cancellation paved with Doppler assimilation for TADS, Mechanical Systems and Signal Processing, 184 (2023), 109727. https://doi.org/10.1016/j.ymssp. 2022.109727
[13] M. A. Z. Raja, M. S. Aslam, N. I. Chaudhary, et al., Design of hybrid nature-inspired heuristics with application to active noise control systems, Neural Computing and Applications, 31 (2019), 2563-2591. https://doi.org/10. 1007/s00521-017-3214-2
[14] D. Sundaram, V. Parthiban, S. Dhanasekar, Controllability criteria for Type-2 fuzzy fractional-order dynamical system via mittag-leffler matrix function using granular derivative, Iranian Journal of Fuzzy Systems, 21(5) (2024), 133-150. https://doi.org/10.22111/ijfs.2024.47862.8422
[15] X. Tian, J. Huang, X. Feng, Y. Shen, An intermittent FxLMS algorithm for active noise control systems with saturation nonlinearity, IEEE/ACM Transactions on Audio, Speech, and Language Processing, 30 (2022), 2347 2356. https://doi.org/10.1109/TASLP.2022.3192725
[16] R. Walia, S. Ghosh, Design of active noise control system using hybrid functional link artificial neural network and finite impulse response filters, Neural Computing and Applications, 32(7) (2020), 2257-2266. https://doi.org/ 10.1007/s00521-018-3697-5
[17] D. Wu, R. Peng, J. M. Mendel, Type-1 and interval type-2 fuzzy systems [AI-eXplained], IEEE Computational Intelligence Magazine, 18(1) (2023), 81-83. https://doi.org/10.1109/MCI.2022.3223496
[18] Y. Yang, X. Li, X. Liu, Event-triggered output quantization control for asynchronously switched interval type 2 fuzzy systems, International Journal of Robust and Nonlinear Control, 34(15) (2024), 10276-10291. https: //doi.org/10.1002/rnc.7516
[19] S. Yu, S. Pang, J. Ning, et al., ANC-Net: A novel multi-scale active noise cancellation network for rotating machinery fault diagnosis based on discrete wavelet transform, Expert Systems with Applications, 265 (2025), 125937. https://doi.org/10.1016/j.eswa.2024.125937
[20] K. Zhang, G. Lyu, X. Luo, A deep recurrent neural network controller for nonlinear active noise control systems, IEEE 6th International Conference on Computer and Communications, (2020), 2393-2396. https://doi.org/10. 1109/ICCC51575.2020.9345164
[21] E. Zhang, Z. Peng, Y. Chen, et al., An improved Takagi–Sugeon–Kang-based FxLMS algorithm and its active noise control in electric bus driving position, Journal of Vibration and Control, (2024). https://doi.org/10.1177/ 10775463241276970
[22] H. Zhang, D. Wang, Deep ANC: A deep learning approach to active noise control, Neural Networks, 141 (2021), 1-10. https://doi.org/10.1016/j.neunet.2021.03.037
[23] Y. Zhang, J. Wen, Y. Han, Adaptive learning based active noise cancellation, Proceedings of the 3rd International Conference on Multimedia and Image Processing, ACM, (2018), 41-45. https://doi.org/10.1145/3195588. 3195591
 [24] B. Zhuang, et. al., Development and experimental verification of an active noise cancellation (ANC) method for non-invasive sensing, Mechanical Systems and Signal Processing, 196 (2023), 110330. https://doi.org/10.1016/ j.ymssp.2023.110330
Iranian Journal of Fuzzy Systems
Volume 23, Issue 6
November and December 2026
Pages 99-111

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