Abstract
Active noise control systems can effectively suppress the impact of low-frequency noise and they have been applied in many fields. Recently, the evolutionary computation algorithm-based active noise control system has attracted considerable attention. To improve the noise reduction performance of the evolutionary computation algorithm-based active noise control system and solve the problem that the system cannot converge again when the path abruptly changes in steady state, we propose the path abruptly change-quantum-behaved particle swarm optimization algorithm. We apply quantum-behaved particle swarm optimization, a global optimization algorithm, to the active noise control system to improve noise reduction performance. In addition, the scheme of detecting the abrupt path change in steady state and performing re-convergence processing is designed to effectively address the problem that the system cannot regain convergence after a path change in steady state. The simulation study demonstrates that the proposed algorithm can efficiently improve noise reduction performance, accurately detect the path change, and re-converge to new global optimization.
Highlights
With increasingly serious noise pollution, active noise control (ANC) system has attracted increasingly more attention
PAC-quantum-behaved particle swarm optimization (QPSO) improves global convergence performance by introducing the QPSO method to the ANC system, which ensures that the particles can converge to a better optimal solution; PAC-QPSO can effectively address the path change and tolerate interference in the environment, which improves the robustness of the system
The contributions of the proposed algorithm on the issue that an evolutionary computation algorithm-based ANC system cannot regain convergence when the path abruptly changes in steady state are summarized as follows: (1) It can accurately identify the abrupt path change in steady state
Summary
With increasingly serious noise pollution, active noise control (ANC) system has attracted increasingly more attention. Global optimization algorithm QPSO is applied to the ANC system to achieve better global convergence and noise reduction performance. This is one contribution of this article. The ANC system based on the evolutionary computation algorithm cannot converge again after the primary or secondary path abruptly changes. PAC-QPSO improves global convergence performance by introducing the QPSO method to the ANC system, which ensures that the particles can converge to a better optimal solution; PAC-QPSO can effectively address the path change and tolerate interference in the environment, which improves the robustness of the system. Simulation results show that compared with the existing methods, PAC-QPSO can improve noise reduction performance and effectively address the path change in steady state. The position XiĂ°1Ă is selected as the respective pbestiĂ°1Ă in the first iteration.< id="472" datadummy="list" id="list3-1461348419901084" list-type="simplelist">
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