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Design of two-channel filter bank using nature inspired optimization based fractional derivative constraints

机译:基于自然启发式优化的分数阶导数约束设计两通道滤波器组

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摘要

In this article, a novel approach for 2-channel linear phase quadrature mirror filter (QMF) bank design based on a hybrid of gradient based optimization and optimization of fractional derivative constraints is introduced. For the purpose of this work, recently proposed nature inspired optimization techniques such as cuckoo search (CS), modified cuckoo search (MCS) and wind driven optimization (WDO) are explored for the design of QMF bank. 2-Channel QMF is also designed with particle swarm optimization (PSO) and artificial bee colony (ABC) nature inspired optimization techniques. The design problem is formulated in frequency domain as sum of L-2 norm of error in passband, stopband and transition band at quadrature frequency. The contribution of this work is the novel hybrid combination of gradient based optimization (Lagrange multiplier method) and nature inspired optimization (CS, MCS, WDO, PSO and ABC) and its usage for optimizing the design problem. Performance of the proposed method is evaluated by passband error (phi(p)). stopband error (phi(s)), transition band error (phi(t)), peak reconstruction error (PRE), stopband attenuation (A(s)) and computational time. The design examples illustrate the ingenuity of the proposed method. Results are also compared with the other existing algorithms, and it was found that the proposed method gives best result in terms of peak reconstruction error and transition band error while it is comparable in terms of passband and stopband error. Results show that the proposed method is successful for both lower and higher order 2-channel QMF bank design. A comparative study of various nature inspired optimization techniques is also presented, and the study singles out CS as a best QMF optimization technique. (C) 2014 ISA. Published by Elsevier Ltd. All rights reserved.
机译:本文介绍了一种基于梯度优化和分数微分约束优化的混合方法的2通道线性相位正交镜像滤波器(QMF)组设计的新方法。出于这项工作的目的,针对QMF库的设计,探索了最近提出的自然启发式优化技术,例如杜鹃搜索(CS),改良的杜鹃搜索(MCS)和风力驱动优化(WDO)。 2通道QMF的设计还结合了粒子群优化(PSO)和人工蜂群(ABC)的自然优化技术。在频域中将设计问题表述为正交频率下通带,阻带和过渡带的L-2误差范数之和。这项工作的贡献是基于梯度的优化(拉格朗日乘数法)和自然启发式优化(CS,MCS,WDO,PSO和ABC)的新颖混合组合,以及其用于优化设计问题的用途。所提出的方法的性能通过通带误差(phi(p))进行评估。阻带误差(phi(s)),过渡带误差(phi(t)),峰值重构误差(PRE),阻带衰减(A(s))和计算时间。设计实例说明了该方法的独创性。还将结果与其他现有算法进行了比较,发现该方法在峰值重构误差和过渡带误差方面提供了最佳结果,而在通带和阻带误差方面具有可比性。结果表明,该方法对于低阶和高阶2通道QMF库设计都是成功的。还介绍了各种受自然启发的优化技术的比较研究,该研究将CS选为最佳QMF优化技术。 (C)2014 ISA。由Elsevier Ltd.出版。保留所有权利。

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