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Development of an embedded Fuzzy-Based Replication Technique for Chaotic System

机译:基于嵌入式模糊系统的混沌系统复制技术的开发

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摘要

This paper addresses an embedded Fuzzy-based replication technique for chaotic dynamic system. It hybridizes the advantages of Fuzzy Logic that is capable of handling complex, nonlinear and sometimes mathematically intangible dynamic systems and the global modeling capability of embedding phase space. The methodology is based on the fundamental characteristics of chaotic time series, which exhibits some stochastic behavior in time domain and its deterministic behavior can be displayed in the embedding phase space. For a given chaotic time series, an embedding phase space is firstly reconstructed by selecting same appropriate phase space points, the membership function in the fuzzy system will be optimized by using a back-propagation adaptive neural-fuzzy system. The proposed method is demonstrated by an example of Mackey-grass chaotic equation. The repredicted time series are favorably compared to the calculated one
机译:本文提出了一种基于模糊的嵌入式混沌动态系统复制技术。它融合了能够处理复杂,非线性甚至是数学上无形的动态系统的模糊逻辑的优势,以及嵌入相空间的全局建模能力。该方法基于混沌时间序列的基本特征,其在时域中表现出一些随机行为,并且其确定性行为可以在嵌入相空间中显示。对于给定的混沌时间序列,首先通过选择相同的适当相空间点来重构嵌入相空间,然后通过使用反向传播自适应神经模糊系统来优化模糊系统中的隶属度函数。通过Mackey-grass混沌方程的实例证明了该方法的有效性。与重新计算的时间序列相比,重新预测的时间序列具有优势

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