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Modified Volterra LMS algorithm to fractional order for identification of Hammerstein non-linear system

机译:将Volterra LMS算法修改为分数阶以识别Hammerstein非线性系统

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In this study, a new non-linear recursive mechanism for Volterra least mean square (VLMS) algorithm is proposed in the domain of non-linear adaptive signal processing and control. The proposed adaptive scheme is developed by applying concepts and theories of fractional calculus in weight adaptation structure of standard VLMS approach. The design scheme based on fractional VLMS (F-VLMS) algorithm is applied to parameter estimation problem of non-linear Hammerstein Box-Jenkins system for different noise and step size variations. The adaptive variables of F-VLMS are compared from actual parameters of the system as well as with the results of conventional VLMS for each case to verify its correctness. Comprehensive statistical analyses are conducted based on sufficient large number of independent runs and performance indices in terms of mean square error, variance account for and Nash-Sutcliffe efficiency establish the worth and effectiveness of the scheme.
机译:在这项研究中,在非线性自适应信号处理和控制领域中,提出了一种新的Volterra最小均方(VLMS)算法的非线性递归机制。提出的自适应方案是通过将分数微积分的概念和理论应用于标准VLMS方法的权重自适应结构中而开发的。将基于分数VLMS(F-VLMS)算法的设计方案应用于非线性Hammerstein Box-Jenkins系统针对不同噪声和步长变化的参数估计问题。将F-VLMS的自适应变量与系统的实际参数进行比较,并与每种情况下常规VLMS的结果进行比较,以验证其正确性。基于足够多的独立运行和性能指标,根据均方误差,方差占和Nash-Sutcliffe效率,进行了全面的统计分析,确定了该方案的价值和有效性。

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