Evolutionary adaptive filtering based on competing filter structures
Evolutionary adaptive filtering based on competing filter structures
复制标题
基于竞争滤波器结构的进化自适应滤波
DOI：
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发表时间：
2011
期刊：
影响因子：
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通讯作者：
Walter Kellermann
中科院分区：
文献类型：
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作者：
M. Zeller;Walter Kellermann
本文提出了一种新的滤波方案，实现了一个通用的，完全自适应的结构，系数和所需的内存大小自动识别。特别地，不区分线性或非线性模型，因为滤波器结构可以演变成线性或二阶沃尔泰拉滤波器。这是通过监测各种组合的混合变量来实现的，其中使用不同大小的竞争过滤器。使用一组直观的规则沿着与所需的步长的内存大小的变化，实现了一个动态增长/收缩的模型结构。通过一个声学回声消除任务，其中考虑现实的线性和非线性系统以及平稳和非平稳输入信号的方法的有效性的快速收敛识别的任意未知系统。
This paper presents a novel filtering scheme that realizes a general, fully adaptive structure where both coefficients and required memory size are identified automatically. In particular, no distinction between linear or nonlinear models is made, since the filter structure can evolve into either a linear or a second-order Volterra filter. This is achieved by monitoring the mixing variables of various combinations where differently-sized competing filters are used. Using a set of intuitive rules along with desired step sizes for memory size changes, a dynamically growing/shrinking model structure is realized. The effectiveness of the approach for a fast-converging identification of arbitrary unknown systems is shown by means of an acoustic echo cancellation task where realistic linear and nonlinear systems as well as stationary and nonstationary input signals are considered.