摘要
In this study, hybrid computational frameworks are developed for active noise control（ANC） systems using an evolutionary computing technique based on genetic algorithms（GAs） and interior-point method（IPM）, following an integrated approach, GA-IPM. Standard ANC systems are usually implemented with the filtered extended least mean square algorithm for optimization of coefficients for the linear finite-impulse response filter, but are likely to become trapped in local minima（LM）. This issue is addressed with the proposed GA-IPM computing approach which is considerably less prone to the LM problem. Also, there is no requirement to identify a secondary path for the ANC system used in the scheme. The design method is evaluated using an ANC model of a headset with sinusoidal, random, and complex random noise interferences under several scenarios based on linear and nonlinear primary and secondary paths. The accuracy and convergence of the proposed scheme are validated based on the results of statistical analysis of a large number of independent runs of the algorithm.
开发了一种主动噪声控制(active noise control,ANC)系统的混合计算框架,运用基于遗传算法(geneticalgorithm,GA)和内点法(interior-point method,IPM)的进化计算技术,集成得到GA-IPM方法。标准ANC系统通常采用滤波扩展最小均方算法优化线性有限脉冲响应滤波器的系数,但易陷入局部极小值(local minima,LM)。本文提出的GA-IPM计算方法有效解决了上述问题。该法不易出现LM问题,且无需识别方案中ANC系统的次级路径。采用正弦、随机和复杂随机噪声干扰下的耳机ANC模型,对该方法在几种线性和非线性主级和次级路径状况下的表现进行评估。大量独立运行算法的统计分析结果验证了该方案的准确性和收敛性。
