期刊名称：International Journal of Computer Science and Information Technologies
电子版ISSN：0975-9646
出版年度：2012
卷号：3
期号：5
页码：5036-5039
出版社：TechScience Publications
摘要：In this paper, we propose a computationally efficient Legendre Neural Network (LNN) for nonlinear Active Noise Cancellation (NANC). Update algorithms for NANC with linear secondary path (LSP) based on Filtered-x Least Mean Square (FXLMS), Filtered-e Least Mean Square(FELMS) and Recursive Least Square(RLS) are developed. Update algorithm for NANC with nonlinear secondary path(NSP) is also developed which rests upon virtual secondary path concept. Performance of the proposed network and algorithms are validated through extensive computer simulations.
