摘要
基于高阶累积量(HOC)的自适应滤波器能够滤除高斯噪声或其它具有对称概率分布函数的噪声,其解法一般采用的是梯度搜索法,但是梯度搜索过程难以避免局部收敛而且计算复杂.粒子群优化算法(PSO)具有算法简洁,易于实现,且不需要梯度信息等优势.使用粒子群优化算法求解高阶累积量自适应滤波器系数优化问题,为滤波器参数的优化提供了一种新的思路.仿真结果表明,使用PSO优化算法求解自适应滤波器系数能获得更高的精度.同时PSO算法受系统跃变的影响较小,因此它在求解非平稳过程模型系统时具有一定的优势.
A high-order cumulant-based （HOC） adaptive filter can limit Gauss noise or other noise with symmetric probability distribution function. Current HOC-based adaptive filter commonly adopts the gradient search method, but it is hard to avoid local convergence and complexity with the gradient search process. Particle swarm optimization （PSO） is simple and easy to implement, and with no gradient information and with other advantages, which can be used to solve many complex problems. Using the PSO algorithm to optimize the filter coefficients was proposed as a new method, considering HOC-based coefficients adjustment of adaptive filter as an optimization problem. The simulation results show that using the PSO can give higher precision in HOC based coefficients optimization of adaptive filter. In addition, the PSO algorithm is relatively little affected by system jump, which has a certain advantage in the non-stationary process model.
出处
《山东大学学报（工学版）》
CAS
2007年第6期15-19,共5页
Journal of Shandong University（Engineering Science）
基金
山东省自然科学基金项目(Y2003G01)
山东省教育厅科技计划项目(J06P53)
