采用遗传算法和LMS算法混合确定未知系统辨识的自适应步长

Proceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP '02. Pub Date : 2002-11-18 DOI:10.1109/ICONIP.2002.1198172

H. Kim, T. Lee, D. Lim, D. Jung

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引用次数: 3

摘要

将遗传算法应用于遗传算法与最小均方算法相结合的自适应有限脉冲响应(FIR)滤波器参数优化问题。对于系统辨识问题，LMS算法计算滤波系数，遗传算法自适应搜索最优步长。由于步长影响系统的稳定性和性能，因此有必要采用能够控制步长的方法。将遗传算法的仿真结果与传统LMS算法进行了比较。我们得到遗传算法在大多数情况下明显优于(精度)。

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The hybrid method for determining an adaptive step size of the unknown system identification using genetic algorithm and LMS algorithm

We describe the application of a genetic algorithm (GA) to the problem of parameter optimization for an adaptive finite impulse response (FIR) filter combining genetic algorithm (GA) and least mean square (LMS) algorithm. For system identification problem, LMS algorithm computes the filter coefficients and GA search the optimal step-size adaptively. Because step-size influences on the stability and performance, so it is necessary to apply method that can control it. The simulation results of the GA were compared to the traditional LMS algorithm. We obtained that genetic algorithm was clearly superior (in accuracy) in most cases.

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Proceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP '02.

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