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Bayesian system identification and chaotic prediction from data for stochastic Mathieu-van der Pol-Duffing energy harvester

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Theoretical & Applied Mechanics Letters 2023年第2期13卷 89-92页

作者： Di Liu Shen Xu Jinzhong Ma School of Mathematics Sciences Shanxi University030006TaiyuanShanxiChina

In this paper,the approximate Bayesian computation combines the particle swarm optimization and se-quential Monte Carlo methods,which identify the parameters of the Mathieu-van der Pol-Duffing chaotic energy harvester *** the proposed method is applied to estimate the coefficients of the chaotic model and the response output paths of the identified coefficients compared with the observed,which verifies the effectiveness of the proposed ***,a partial response sample of the regular and chaotic responses,determined by the maximum Lyapunov exponent,is applied to detect whether chaotic motion occurs in them by a 0-1 *** paper can provide a reference for data-based parameter iden-tification and chaotic prediction of chaotic vibration energy harvester systems.

关键词： Vibration energy harvester Approximate Bayesian computation 0–1 test Parameter identification chaotic prediction

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Rock burst chaotic prediction on Multivariate Time Series and LSSVR 25th

Rock burst Chaotic Prediction on Multivariate Time Series an...

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第25届中国控制与决策会议

作者： Wang Wei Tao Hui Ma Xiao-ping School of Information and Electrical Engineering China University of Mining and Technology School of Electrical Engineering and Automation Henan Polytechnic University

ISBN: (纸本)9781467355339

State variables reconstructed by multivariate time series were used as LSSVR model inputs to predict the future value of rock burst monitor variables. First, the chaotic prediction principle on multivariate reconstruction and LSSVR was given. Then given the effects of reconstruction parameters on reconstruction results and LSSVR parameters on prediction error, genetic algorithm was adopted to determine reconstruction and LSSVR parameters simultaneously to ensure chaotic prediction accuracy. Finally, in Matlab2009b environment, based on the effectiveness verify of our method by Lorenz chaos system, Microseism time series were simulated to predict rock burst. The results show that the rock burst prediction method on multivariate time series reconstruction and LSSVR can accurately predict monitoring variables in advance to forecast rock burst even in the case of relatively short history data.

关键词： Rock burst chaotic prediction multivariate time series phase space reconstruction LSSVR genetic algorithm

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Method of chaotic prediction Based on Wavelet BP Network and Its Application

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Journal of Systems Science and Information 2008年第1期6卷 55-61页

作者： Qiang Fu Guoliang Li Zilong Wang College of Water Conservancy and Civil Engineering Northeast Agricultural University Harbin 150030 China

The advantage of artificial neural network and wavelet analysis are integrated through replacing the traditional S-shaped activation function with the wavelet function. One method of chaotic prediction based on wavelet BP network was put forward based on the reconstruction of state space. Training data construction and networks structure are determined by chaotic phase space, and nonlinear relationship of phase points was established by BP neural networks. As an example, the new method was applied on short term forecasting of monthly precipitation time series of Sanjiang Plain with chaotic characteristics. The results showed so higher precision of the method had that the theoretical evidence would be provided for applying the chaos theory to study the variable law of monthly precipitation.

关键词： BP network wavelet analysis chaotic prediction monthly precipitation time series

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chaotic Control of Network Traffic

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China Communications 2010年第5期7卷 112-119页

作者： Yang Tan Guo Shize Jin Yuehui Cheng Shiduan School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu 611731 P. R. China State Key Laboratory of Networking and Switching Beijing University of Posts and Telecommunications Beijing 100876 P. R. China Information Security Center Beijing University of Posts and Telecommunications Beijing 100876 P. R. China

This paper presents a chaotic control method on network traffic. By this method, the chaotic network traffic can be controlled to pre-assigned equifibrium point according to chaotic prediction and the Largest Lyapunov Exponent （LLE） of the traffic on congested link is reduced, thereby the probability of traffic burst and network congestion can be reduced. Numerical examples show that this method is effective.

关键词： network traffic chaotic control Lyapunov exponent chaotic prediction

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Joint Network Traffic Forecast with ARIMA models and chaotic Models Based on Wavelet Analysis

Joint Network Traffic Forecast with ARIMA models and Chaotic...

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The International Conference on Recent Trends in Materials and Mechanical Engineering (ICRTMME 2011)

作者： Mingke Dong Chen Chen Minhua Huang Ye Jin School of Electronics Engineering and Computer Science Peking University

In the recent study of network traffic,it is shown that the traffic flow presents both periodic and self-similar *** to these two features,the short-term forecast of network traffic cannot be accurately fit in either autoregressive integrated moving average(ARIMA) models which is suitable for linear behavior,or chaotic models which is corresponding to self-similarity *** this paper,our methodology suggests that by using wavelet multiresolution analysis,we can obtain a joint short-term network traffic prediction method and get a more precise forecast result as compared to using either ARIMA models or chaotic *** also run simulations to show the improvement of prediction accuracy of our proposed approach.

关键词： network traffic prediction chaotic prediction wavelet analysis

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