Fully adaptive neural nonlinear FIR filters

W. C. Siaw, S. L. Goh, A. I. Hanna, Christos Boukis, D. Mandic
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引用次数: 1

Abstract

A class of algorithms for training neural adaptive filters employed for nonlinear adaptive filtering is introduced. Sign algorithms incorporated with the fully adaptive normalised nonlinear gradient descent (SFANNGD) algorithm, normalised nonlinear gradient descent (SNNGD) algorithm and nonlinear gradient descent (SNGD) algorithm are proposed. The SFANNGD, SNNGD and the SNGD are derived based upon the principle of the sign algorithm used in the least mean square (LMS) filters. Experiments on nonlinear signals confirm that SFANNGD, SNNGD and the SNGD algorithms perform on par as compared to their basic algorithms but the sign algorithm decreases the overall computational complexity of the adaptive filter algorithms.
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全自适应神经非线性FIR滤波器
介绍了一类用于非线性自适应滤波的神经自适应滤波器的训练算法。提出了结合全自适应归一化非线性梯度下降(SFANNGD)算法、归一化非线性梯度下降(SNNGD)算法和非线性梯度下降(SNGD)算法的符号算法。SFANNGD、SNNGD和SNGD是基于最小均方(LMS)滤波器中使用的符号算法的原理推导出来的。在非线性信号上的实验证实,SFANNGD、SNNGD和SNGD算法的性能与其基本算法相当,但符号算法降低了自适应滤波算法的总体计算复杂度。
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