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Modeling of memristor-based chaotic systems using nonlinear Wiener adaptive filters based on backslash operator

机译:基于反斜杠算子的非线性维纳自适应滤波器基于忆阻器的混沌系统建模

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摘要

Memristor-based chaotic systems have complex dynamical behaviors, which are characterized as non-linear and hysteresis characteristics. Modeling and identification of their nonlinear model is an important premise for analyzing the dynamical behavior of the memristor-based chaotic systems. This paper presents a novel nonlinear Wiener adaptive filtering identification approach to the memristor-based chaotic systems. The linear part of Wiener model consists of the linear transversal adaptive filters, the nonlinear part consists of nonlinear adaptive filters based on the backslash operator for the hysteresis characteristics of the memristor. The weight update algorithms for the linear and nonlinear adaptive filters are derived. Final computer simulation results show the effectiveness as well as fast convergence characteristics. Comparing with the adaptive nonlinear polynomial filters, the proposed nonlinear adaptive filters have less identification error. (C) 2016 Elsevier Ltd. All rights reserved.
机译:基于忆阻器的混沌系统具有复杂的动力学行为,其表现为非线性和滞后特性。它们的非线性模型的建模和识别是分析基于忆阻器的混沌系统动力学行为的重要前提。本文提出了一种基于忆阻器的混沌系统非线性维纳自适应滤波辨识方法。 Wiener模型的线性部分包括线性横向自适应滤波器,非线性部分包括基于反斜杠运算符的忆阻器滞回特性的非线性自适应滤波器。推导了线性和非线性自适应滤波器的权重更新算法。最终的计算机仿真结果显示了有效性以及快速收敛的特性。与自适应非线性多项式滤波器相比,提出的非线性自适应滤波器识别误差小。 (C)2016 Elsevier Ltd.保留所有权利。

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