检索结果-南通市图书馆

Research on Chaotic Time Series Prediction Based on K-entropy and rbf neural networks

在线全文

学校读者我要写书评

暂无评论

Journal of Systems Science and Information 2006年第4期4卷 741-748页

作者： Xiu Yan Junhai Ma Management School Tianjin University Tianjin 300072 China Department of Foundation Tianjin Urban Construction Institute Tianjin 300384 China

In this paper, a method of direct multi-step prediction of chaotic time series is proposed, which is based on Kolmogorov entropy and radial basis functions neural networks. This is done first by reconstructing a phase space using chaotic time series, then using K-entropy as a quantitative parameter to obtain the maximum predictability time of chaotic time series, finally the predicted chaotic time series data can be acquired by using rbfNN. The application considered is Lorenz system. Simulation results for direct multi-step prediction method are compared with recurrence multi-step prediction method. The results indicate that the direct multi-step prediction is more accurate and rapid than the recurrence multi-step prediction within the maximum predictability time of chaotic time series. So, it is convenient to forecast and control with real time using the method of direct multi-step prediction.

关键词： Kolmogorov entropy chaotic time series rbf neural networks multi-step prediction

Calibration Method Based on rbf neural networks for Soil Moisture Content Sensor

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Agricultural Science & Technology 2010年第2期11卷 140-142页

作者：杨敬锋李亭卢启福陈志民广东融讯信息科技有限公司广东广州510656 广东瑞图万方科技股份有限公司广东顺德528305 中山火炬职业技术学院广东中山528436 华南农业大学公共基础课实验教学中心广东广州510642

Temporal and spatial variation of soil moisture content is significant for crop growth,climate change and the other *** order to overcome shortage of non-linear output voltage of TDR3 soil moisture content sensor and increase soil moisture content data collection and computational efficiency,this paper presents a rbf neural network calibration method of soil moisture content based on TDR3 soil moisture sensor and wireless sensor *** results show that the calibration method is effective...

关键词： Calibration Model Soil Moisture Sensor Wireless Sensor networks rbf neural networks

State Feedback Control For A Class Of Switched Nonlinear Systems Based On rbf neural networks

在线全文

学校读者我要写书评

暂无评论

State Feedback Control For A Class Of Switched Nonlinear Sys...

Prediction of Specific Region Cloud Cover Based on rbf neural networks

第二十三届中国控制会议

作者： Long Fei Fei Shumin (Department of Automatic control. Southeast University, Nanjing, 210096,China)

＜正＞ An adaptive state feedback control scheme and a switching strategy for the output tracking of switched nonlinear systems are presented. The rbf neural network is used to adaptive compensate for the nonlinear part of switched systems, and the approximation errors of the rbf neural networks are introduced to the adaptive law in order to improve the quality of the systems. The controller and switching strategy can guarantee asymptotic stability of tracking error for the overall system.

关键词： Switched Nonlinear Systems rbf neural networks Adaptive Control State Feedback.

来源： cnki会议评论

在线全文

cnki会议

学校读者我要写书评

暂无评论

Prediction of Specific Region Cloud Cover Based on RBF Neura...

Radial Basis Function neural networks-Based Modeling of the Membrane Separation Process: Hydrogen Recovery from Refinery Gases

2010 Second IITA International Conference on Geoscience and Remote Sensing (IITA-GRS 2010)

作者： Fang Lu Beijing Institute of Applied Meteorology Beijing China Zhigang Wang1 2 1. School of Mechatronical Engineering 2. Lab of Computer Network Defense Technology Beijing Institute of Technology Beijing China

This paper presents a method based on rbf neural networks to predict the specific region cloud cover, which has the feature of time serial and nonlinear. Using this method, we can train the rbf neural networks and find out the nonlinear relations among historical cloud cover data, to predict future time of cloud cover. Experiment results show that the method provided a kind of viable intelligent way for the quantitative prediction of cloud cover.

关键词： prediction of cloud cover GMS-5 rbf neural networks

来源： cnki会议评论

在线全文

cnki会议

学校读者我要写书评

暂无评论

Journal of Natural Gas Chemistry 2006年第3期15卷 230-234页

作者： Lei Wang Cheng Shao Hai Wang Hong Wu Institute of Advanced Control Technology Dalian University of Technology Dalian 116024 China Tianbang National Engineering Research Center of Membrane Technology Co. Ltd Dalian 116023 China Sinopec Zhenhai Refining and Chemical Company Limited Ningbo 315200 China

Membrane technology has found wide applications in the petrochemical industry, mainly in the purification and recovery of the hydrogen resources. Accurate prediction of the membrane separation performance plays an important role in carrying out advanced process control （APC）. For the first time, a soft-sensor model for the membrane separation process has been established based on the radial basis function （rbf） neural networks. The main performance parameters, i.e, permeate hydrogen concentration, permeate gas flux, and residue hydrogen concentration, are estimated quantitatively by measuring the operating temperature, feed-side pressure, permeate-side pressure, residue-side pressure, feed-gas flux, and feed-hydrogen concentration excluding flow structure, membrane parameters, and other compositions. The predicted results can gain the desired effects. The effectiveness of this novel approach lays a foundation for integrating control technology and optimizing the operation of the gas membrane separation process.

关键词： membrane separation hydrogen recovery soft sensor rbf neural networks refinery operation optimization

Component Content Soft-Sensor Based on Hybrid Models in Countercurrent Rare Earth Extraction Process

在线全文

学校读者我要写书评

暂无评论

Journal of Rare Earths 2005年第S1期23卷 86-91页

作者：杨辉王欣 School of Electrical and Electronics Engineering East China Jiaotong University Nanchang 330013 China Center of Electrical & Electronic Technology Shanghai Jiaotong University Shanghai 200030 China

In consideration of the online measurement of the component content in rare earth countercurrent extraction separation process, the soft sensor method based on hybrid modeling was proposed to measure the rare earth component content. The hybrid models were composed of the extraction equilibrium calculation model and the Radial Basis Function (rbf) neural Network (NN) error compensation model; the parameters of compensation model were optimized by the hierarchical genetic algorithms (HGA). In addition, application experiment research of this proposed method was carried out in the rare earth separation production process of a corporation. The result shows that this method is effective and can realize online measurement for the component content of rare earth in the countercurrent extraction.

关键词： countercurrent extraction soft-sensor equilibrium calculation model rbf neural networks hierarchical genetic algorithms rare earths

Adaptive tracking H-infinity control for switched nonlinear systems with unknown control gain sign

在线全文

学校读者我要写书评

暂无评论

控制理论与应用（英文版） 2011年第4期9卷 579-583页

作者： LeiYU ShuminFEI HairongZHU XunLI School of Mechanical and Electrical Engineering Soochow UniversitySuzhou Jiangsu 215021China SchoolofAutomation SoutheastUni＇JersityNanjingJiangsu210096China

This paper addresses the adaptive tracking control scheme for switched nonlinear systems with unknown control gain sign. The approach relaxes the hypothesis that the upper bound of function control gain is known constant and the bounds of external disturbance and approximation errors of neural＇networks are known. rbf neural networks （NNs） are used to approximate unknown functions and an H-infinity controller is introduced to enhance robustness. The adaptive updating laws and the admissible switching signals have been derived from switched multiple Lyapunov function method. It＇s proved that the resulting closed loop system is asymptotically Lyapunov stable such that the output tracking error performance and H-infinity disturbance attenuation level are well obtained. Finally, a simulation example of Forced Duffing systems is given to illustrate the effectiveness of the proposed control scheme and improve significantly the transient performance.

关键词： rbf neural networks Adaptive tracking control H-infinity control Tracking error performance

A Novel rbf neural Network Based on Data Dispersion Level and Its Application in BOF Endpoint Prediction 25th

在线全文

学校读者我要写书评

暂无评论

A Novel RBF Neural Network Based on Data Dispersion Level an...

Semi-analytical steady-state response prediction for multi-dimensional quasi-Hamiltonian systems

第25届中国控制与决策会议

作者： Zhang Yu-xian Liu Tong Wang Jian-hui School of Electrical Engineering Shenyang University of Technology College of Information Science and Engineering Northeastern University

ISBN: (纸本)9781467355339

The Basic Oxygen Furnace （BOF） process is a primary method of steel-making. The endpoint targets must be strictly control. However, it is difficult to accurately predict endpoint targets in BOF. In this paper, a clustering method is proposed in which data dispersion level and new metric are introduced respectively. And the proposed clustering method is applied to obtain accurate neural network centers in order to improve accuracy of Radial Basis Function （rbf） neural network. Then a novel rbf neural network is built for the endpoint prediction in BOF process. Finally, an example of endpoint prediction is shown, the simulation results indicate that the influence of disperse and noisy data is decreased, clustering accuracy is increased and the accuracy of endpoint prediction based on rbf neural networks is improved.

关键词： Basic Oxygen Furnace Endpoint Prediction rbf neural networks Clustering Data Dispersion Level

来源： cnki会议评论

在线全文

cnki会议

学校读者我要写书评

暂无评论

Chinese Physics B 2023年第6期32卷 177-186页

作者：叶文伟陈林聪原子钱佳敏孙建桥 College of Civil Engineering Huaqiao UniversityXiamen 361021China Key Laboratory for Intelligent Infrastructure and Monitoring of Fujian Province Huaqiao UniversityXiamen 361021China Department of Mechanical Engineering School of Engineering University of California MercedCA 95343USA

The majority of nonlinear stochastic systems can be expressed as the quasi-Hamiltonian systems in science and engineering. Moreover, the corresponding Hamiltonian system offers two concepts of integrability and resonance that can fully describe the global relationship among the degrees-of-freedom(DOFs) of the system. In this work, an effective and promising approximate semi-analytical method is proposed for the steady-state response of multi-dimensional quasi-Hamiltonian systems. To be specific, the trial solution of the reduced Fokker–Plank–Kolmogorov(FPK) equation is obtained by using radial basis function(rbf) neural networks. Then, the residual generated by substituting the trial solution into the reduced FPK equation is considered, and a loss function is constructed by combining random sampling technique. The unknown weight coefficients are optimized by minimizing the loss function through the Lagrange multiplier method. Moreover, an efficient sampling strategy is employed to promote the implementation of algorithms. Finally, two numerical examples are studied in detail, and all the semi-analytical solutions are compared with Monte Carlo simulations(MCS) results. The results indicate that the complex nonlinear dynamic features of the system response can be captured through the proposed scheme accurately.

关键词： steady-state response quasi-Hamiltonian systems FPK equation rbf neural networks