Predict the Time Series of the Parameter-Varying Chaotic System Based on Reduced Recursive Lease Square Support Vector Machine

Yang, Zhonghui Ou; Wang, Yongsheng; Li, Dedong; Wang, Changjin

doi:10.1109/aici.2009.324

Cited by 8 publications

(5 citation statements)

References 9 publications

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“…14 With time series simulated from the chaotic Ikeda map, a MARE value as low as 5.8 × 10 −5 was reported in Ref. 38. In general, the prediction of noisy (possibly chaotic) real-world dynamics yields larger errors than the prediction of synthetic numerical data without noise.…”

Section: Article Scitationorg/journal/chamentioning

confidence: 95%

Machine learning algorithms for predicting the amplitude of chaotic laser pulses

Amil

Soriano

Masoller

2019

Chaos: An Interdisciplinary Journal of Nonlinear Science

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Section: Article Scitationorg/journal/chamentioning

confidence: 95%

Machine learning algorithms for predicting the amplitude of chaotic laser pulses

Amil

Soriano

Masoller

2019

Chaos: An Interdisciplinary Journal of Nonlinear Science

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“…On the other hand, Liu and Yao employed a hybrid system including PSO and the least square SVM to perform prediction processes [29]. Readers are also referred to [30][31][32][33][34][35][36] to get a better idea about using SVM in prediction problems.…”

Section: A Brief Review Of the Literaturementioning

confidence: 99%

An Ant-Lion Optimizer-Trained Artificial Neural Network System for Chaotic Electroencephalogram (EEG) Prediction

Köse

2018

Applied Sciences

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The prediction of future events based on available time series measurements is a relevant research area specifically for healthcare, such as prognostics and assessments of intervention applications. A measure of brain dynamics, electroencephalogram time series, are routinely analyzed to obtain information about current, as well as future, mental states, and to detect and diagnose diseases or environmental factors. Due to their chaotic nature, electroencephalogram time series require specialized techniques for effective prediction. The objective of this study was to introduce a hybrid system developed by artificial intelligence techniques to deal with electroencephalogram time series. Both artificial neural networks and the ant-lion optimizer, which is a recent intelligent optimization technique, were employed to comprehend the related system and perform some prediction applications over electroencephalogram time series. According to the obtained findings, the system can successfully predict the future states of target time series and it even outperforms some other hybrid artificial neural network-based systems and alternative time series prediction approaches from the literature.

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“…If the structure of the chaotic system is unknown, a dynamic model should be constructed to simulate the behavior of the original chaotic system for the purpose of prediction and identification [16,17]. Because of the nonlinear nature of chaotic systems, many nonlinear models such as neural networks (NNs) [10,18], fuzzy logic systems (FLSs) [19], support vector machines (SVMs) [20], and their combinations [21,22] have been proposed to predict the chaotic time series or identify the chaotic system. However, the approaches mentioned above involve complete black-box identification, in which only the states of the chaotic system are utilized while little structure information of the system is taken into account.…”

Section: Introductionmentioning

confidence: 99%

Chaotic system identification using Wiener‐LSSVM model

Wang

Shu-en

Liu

et al. 2016

IEEJ Transactions Elec Engng

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In this paper, a new method is proposed for identifying chaotic system based on a Wiener-least squares support vector machine (Wiener-LSSVM) model. The model consists of a linear dynamic subsystem followed by a static nonlinear function, which is represented by LSSVM in this paper. The parameters of the linear dynamic part and those of LSSVM are estimated simultaneously by solving a set of linear equations using the least squares (LS) method. The proposed method incorporates partial structure information into the identification process and does not assume that the parameters of linear dynamic part are known. On the other hand, the LS algorithm is more efficient than gradient-descendent-based algorithms for estimating the parameters of Wiener-LSSVM. Three identification examples are given to validate the effectiveness of the proposed method.

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Predict the Time Series of the Parameter-Varying Chaotic System Based on Reduced Recursive Lease Square Support Vector Machine

Cited by 8 publications

References 9 publications

Machine learning algorithms for predicting the amplitude of chaotic laser pulses

Machine learning algorithms for predicting the amplitude of chaotic laser pulses

An Ant-Lion Optimizer-Trained Artificial Neural Network System for Chaotic Electroencephalogram (EEG) Prediction

Chaotic system identification using Wiener‐LSSVM model

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