The Prediction Model of Chaotic Series Based on Support Vector Machine and its Application to Runoff

2011 ◽  
Vol 255-260 ◽  
pp. 3594-3599 ◽  
Author(s):  
Guo Rong Yu ◽  
Ju Rui Yang ◽  
Zi Qiang Xia
Keyword(s):  
Time Series ◽  
Support Vector Machine ◽  
Phase Space ◽  
Prediction Model ◽  
Linear Prediction ◽  
Linear Time ◽  
Surface Flow ◽  
Support Vector ◽  
Good Prediction ◽  
Non Linear

Chaos and support vector machine theory has opened up a new route to study complicated and changeable non-linear hydrology time series. Applying the Chaos and non-linear time series based on the support vector machine regression principle, this paper proposes a method and its characteristic and the choosing of key parameters to forecast and set up models. According to Phase Space Reconstruction theory carry on reconstruction of Phase Space to monthly surface flow course, have discussed that probed into the non-linear prediction model of time series of Chaos of the support vector machine, application in the monthly surface flow, have introduce it through to the nuclear function of the base in the course of setting up the model of support vector machine, has simplified the course of solving the non-linear problems. The instance indicates that the model can deal with the complicated hydrology data array well, and there is the good prediction precision.

scholarly journals Chaotic Vibration Prediction of a Free-Floating Flexible Redundant Space Manipulator

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Congqing Wang ◽  
Linfeng Wu
Keyword(s):  
Time Series ◽  
Phase Space ◽  
Prediction Model ◽  
Dynamic Model ◽  
Chaotic Time Series ◽  
Joint Torque ◽  
Support Vector ◽  
Space Manipulator ◽  
Chaotic Vibration ◽  
Vibration Prediction

The dynamic model of a planar free-floating flexible redundant space manipulator with three joints is derived by the assumed modes method, Lagrange principle, and momentum conservation. According to minimal joint torque’s optimization (MJTO), the state equations of the dynamic model for the free-floating redundant space manipulator are described. The PD control using the tracking position error and velocity error in the manipulator is introduced. Then, the chaotic dynamic behavior of the manipulator is analyzed by chaotic numerical methods, in which time series, phase plane portrait, Poincaré map, and Lyapunov exponents are used to analyze the chaotic behavior of the manipulator. Under certain conditions for the joint torque optimization and initial values, chaotic vibration motion of the space manipulator can be observed. The chaotic time series prediction scheme for the space manipulator is presented based on the theory of phase space reconstruction under Takens’ embedding theorem. The trajectories of phase space can be reconstructed in embedding space, which are equivalent to the original space manipulator in dynamics. The one-step prediction model for the chaotic time series and the chaotic vibration was established by using support vector regression (SVR) prediction model with RBF kernel function. It has been proved that the SVR prediction model has a good performance of prediction. The experimental results show the effectiveness of the presented method.

scholarly journals An Interval Forecasting Model Based on Phase Space Reconstruction and Weighted Least Squares Support Vector Machine for Time Series of Dissolved Gas Content in Transformer Oil

Energies ◽  
2020 ◽  
Vol 13 (7) ◽  
pp. 1687
Author(s):  
Fang Yuan ◽  
Jiang Guo ◽  
Zhihuai Xiao ◽  
Bing Zeng ◽  
Wenqiang Zhu ◽  
...  
Keyword(s):  
Time Series ◽  
Support Vector Machine ◽  
Phase Space ◽  
Least Squares ◽  
Weighted Least Squares ◽  
Transformer Oil ◽  
Gas Content ◽  
Support Vector ◽  
Forecasting Model ◽  
Dissolved Gas

Transformer state forecasting and fault forecasting are important for the stable operation of power equipment and the normal operation of power systems. Forecasting of the dissolved gas content in oil is widely conducted for transformer faults, but its accuracy is affected by data scale and data characteristics. Based on phase space reconstruction (PSR) and weighted least squares support vector machine (WLSSVM), a forecasting model of time series of dissolved gas content in transformer oil is proposed in this paper. The phase spaces of time series of the dissolved gas content sequence are reconstructed by chaos theory, and the delay time and dimension are obtained by the C-C method. The WLSSVM model is used to forecast time series of dissolved gas content, the chemical reaction optimization (CRO) algorithm is used to optimize training parameters, the bootstrap method is used to build forecasting intervals. Finally, the accuracy and generalization ability of the forecasting model are verified by the analysis of actual case and the comparison of different models.

scholarly journals Prediction of Energy-Efficient Production of Coalbed Methane Based on Chaotic Time Series and Bayes-Least Squares-Support Vector Machine

2020 ◽  
Vol 38 (4) ◽  
pp. 933-940
Author(s):  
Yan Wang ◽  
Zhongshui Man ◽  
Meihua Lu
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Support Vector Machine ◽  
Phase Space ◽  
Energy Efficient ◽  
Coalbed Methane ◽  
Phase Space Reconstruction ◽  
Support Vector ◽  
Proposed Model ◽  
Bayesian Evidence

The productivity of coalbed methane (CBM) depends heavily on the heat environment, and directly reflects the quality of the well. Following the theories of phase space reconstruction and Bayesian evidence framework, this paper puts forward a Bayes-least squares-support vector machine (Bayes-LS-SVM) model for the prediction of energy-efficient productivity of CBM under Bayesian evidence network based on chaotic time series. The energy-efficient productivity stands for the gas and water production of CBM wells at a low energy consumption, despite the disturbance from the heat environment. The proposed model avoids the local optimum trap of backpropagation neural network (BPNN), and overcomes the main defects of the SVM: high time consumption of parameter determination, and proneness to overfitting. In our model, the model parameters are optimized through three-layer Bayesian evidence inference, and the input vector for prediction is selected adaptively. In this way, the model construction is not too empirical, and the constructed model is highly adaptive. Then, the theory on phase space reconstruction was applied to investigate the chaotic property of the time series on CBM production, and the Bayes-LS-SVM was adopted to predict the time series after phase space reconstruction, in comparison with neural network prediction methods like SVM and BPNN. Experimental results show that the proposed model boast quick computing, accurate fitting, flexible structure, and strong generalization ability.

scholarly journals Fast Prediction with Sparse Multikernel LS-SVR Using Multiple Relevant Time Series and Its Application in Avionics System

2015 ◽  
Vol 2015 ◽  
pp. 1-10
Author(s):  
Yang M. Guo ◽  
Pei He ◽  
Xiang T. Wang ◽  
Ya F. Zheng ◽  
Chong Liu ◽  
...  
Keyword(s):  
Time Series ◽  
Prediction Model ◽  
Prediction Accuracy ◽  
Mean Squared Error ◽  
Computation Time ◽  
Support Vector ◽  
Good Prediction ◽  
Trend Prediction ◽  
Good Prediction Accuracy ◽  
Fast Prediction

Health trend prediction is critical to ensure the safe operation of highly reliable systems. However, complex systems often present complex dynamic behaviors and uncertainty, which makes it difficult to develop a precise physical prediction model. Therefore, time series is often used for prediction in this case. In this paper, in order to obtain better prediction accuracy in shorter computation time, we propose a new scheme which utilizes multiple relevant time series to enhance the completeness of the information and adopts a prediction model based on least squares support vector regression (LS-SVR) to perform prediction. In the scheme, we apply two innovative ways to overcome the drawbacks of the reported approaches. One is to remove certain support vectors by measuring the linear correlation to increase sparseness of LS-SVR; the other one is to determine the linear combination weights of multiple kernels by calculating the root mean squared error of each basis kernel. The results of prediction experiments indicate preliminarily that the proposed method is an effective approach for its good prediction accuracy and low computation time, and it is a valuable method in applications.

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