Active noise control (ANC) represents an efficient technology for enhancing the noise suppression performance and ensuring the stable operation of multi-sensor systems through generative model-enhanced data representation and dynamic information fusion across heterogeneous sensors due to the complexity of the real-world environment. To address problems caused by a nonlinear noise source, a novel adaptive neuro-fuzzy network controller is proposed for feedforward nonlinear ANC systems based on a variable step-size filtered-x least-mean-square (VSS-LMS) algorithm. Specifically, the LMS algorithm is first introduced to update the weight parameters of the controller based on the adaptive neuro-fuzzy network. Then, a variable step-size adjustment strategy is proposed to calculate the learning gain used in the LMS algorithm, which aims to improve the nonlinear noise suppression performance. Additionally, the stability of the proposed method is proven by the discrete Lyapunov theorem. Extensive simulation experiments show that the proposed method surpasses the mainstream ANC methods with regard to nonlinear noise.

Active noise control (ANC) technology has extensive applications in suppressing sound pollution in the real-world environment. In this paper, a new adaptive neuro-fuzzy network (ANFN)-based controller is presented and integrated into hybrid active noise control (HANC) systems to improve the robustness and effectiveness of active noise suppression. Specifically, an adaptive neural network is constructed to minimize the mean square error information with respect to the residual noise. Moreover, a fuzzy logic strategy is proposed to address the manual fine-tuning and nonlinearities encountered in a complex environment. Finally, the stability of the proposed control method is proved by using the Lyapunov theorem. Comparative numerical simulations are given to verify the effectiveness and superiority of the proposed method under different noise signals.

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