OS3-5 Adaptive CMAC Filter for Chaotic Time Series Prediction

Title:	OS3-5 Adaptive CMAC Filter for Chaotic Time Series Prediction
Publication:	ICAROB2017
Volume:	22
Pages:	54-58
ISSN:	2188-7829
DOI:	10.5954/ICAROB.2017.OS3-5
Author(s):	Wei-Lung Mao, Suprapto , Chung-Wen Hung
Publication Date:	January 19, 2017
Keywords:	Chaotic signal, Mackey-glass time series, signal prediction, evolutionary algorithm (EA), biogeographybased optimization (BBO), cerebellar model articulation controller (CMAC)
Abstract:	Chaotic signal is a natural phenomenon exhibiting in every condition of dynamical system. Chaotic signals are almost unpredictable, noise-like, uncertain and irregular behavior, yet they are very useful in numerous applications of signal processing. Due to their behaviors, the quest of a good method to model and analyze of the chaotic signal is very crucial. This paper present a novel strategy to analyze chaotic signal using the cerebellar model articulation controller (CMAC) network combined with evolutionary algorithms (EAs) such as biogeography-based optimization (BBO), genetic algorithm (GA) and particle swarm optimization (PSO). Mackey-glass chaotic signal time series is tested and demonstrated by the conventional and the proposed algorithms. They are compared with each other to determine the optimal filtering and prediction. The result demonstrated that the CMAC combined with EAs could filter, predict and estimate chaotic signal time series well rather than the conventional methods. The best result of the algorithms tested for chaotic signal time series is the CMAC combined with BBO algorithm.
PDF File:	https://alife-robotics.co.jp/members2017/icarob/data/html/data/OS_pdf/OS3/OS3-5.pdf
Copyright:	© The authors. This article is distributed under the terms of the Creative Commons Attribution License 4.0, which permits non-commercial use, distribution and reproduction in any medium, provided the original work is properly cited. See for details: https://creativecommons.org/licenses/by-nc/4.0/

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