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An algorithm for diagnosing nonlinear characteristics of dynamic systems with the integrated periodicity ratio and lyapunov exponent methods

机译:一种诊断具有集成周期性比和Lyapunov指数方法的动态系统非线性特性的算法

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摘要

The present research aims to develop a novel approach for diagnosing the nonlinear behavior of dynamic systems with an algorithm integrating the Periodicity Ratio (P-R) and Lyapunov exponent methods. The advantages and disadvantages of the nonlinear behavior diagnostic methodologies with separate employment of the two methods are studied and compared. Although the two methods generally show effectiveness in diagnosing the characteristics of nonlinear dynamic systems, in some cases both methods may miss or misinterpret some of the characteristics of the nonlinear systems. In fact, the two methods can be complementary as identified in this research. With a specified integration of the two methods, the proposed algorithm maintains the advantages and overcomes the shortcomings of the two methods. The proposed algorithm therefore provides higher completeness, efficiency and accuracy to diagnose the nonlinear characteristics in dynamic systems compared with the existing methods such as the Lyapunov exponent method and the P-R method. The algorithm of the proposed approach is presented in detail with a case study to demonstrate its application. (C) 2019 Elsevier B.V. All rights reserved.
机译:本研究旨在开发一种用于诊断动态系统的非线性行为的新方法,其具有整合周期性比(P-R)和Lyapunov指数方法的算法。研究了非线性行为诊断方法的优点和缺点,并进行了两种方法的单独就业。虽然这两种方法通常表现出诊断非线性动态系统特性的有效性,但在某些情况下,两种方法可能会错过或误解非线性系统的一些特征。事实上,这两种方法可以是互补的,如本研究所识别的那样。通过两种方法的指定集成,所提出的算法保持了优势并克服了两种方法的缺点。因此,该算法提供了更高的完整性,效率和准确性,以诊断动态系统中的非线性特性与Lyapunov指数方法和P-R方法等现有方法相比。通过案例研究详细介绍了所提出的方法的算法,以证明其应用。 (c)2019 Elsevier B.v.保留所有权利。

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