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2011 China control and Decision Conference(2011中国控制与决策会议 CCDC)

作者： Guolian Hou Jianhua Zhang Rui Wang Jian Han Beijing Key Laboratory of Industrial Process Measurement & Control New Technology and System North Beijing Key Laboratory of Industrial Process Measurement & Control New Technology and System North

Considering the electricity price’s volatility and various elements which affect the price in the electricity market, the paper presents hybrid model for the day-ahead electricity market clearing price forecasting. The paper adopts autoregressive moving average (ARMAX) model to reveal the linear relationship between power load and electricity price;the generalized autoregressive conditional heteroskedasticity (GARCH) model to reveal the heteroskedasticity properties of residual. Simultaneously the paper presents the inexactness and irrationality that modeling by the historical data long ago to forecast the price with the change of the time, then presents the rolling forecast that constantly using the latest data to modeling the ARMAX-AR-GARCH model. To reveal the nonlinear relationship between power load and electricity price, the paper adopts least squares support vector machine (LS-SVM). Using the proposed method, the day-ahead electricity prices of California electricity market are forecasted, prediction results show the efficiency of the proposed method.

关键词： day-ahead price forecast ARMAX GARCH rolling forecast LS-SVM

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Path planning of robotic fish based on genetic algorithm and modified dynamic programming

Path planning of robotic fish based on genetic algorithm and...

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2011 International Conference on Advanced Mechatronic systems, ICAMechS 2011

作者： Yang, Qian Yu, Mei Liu, Shu Chai, Zhong-Ming School of Control and Computer Engineering North China Electric Power University Beijing 102206 China Key Laboratory of Measurement and Control New Technology and System for Industrial Process North China Electric Power University Beijing 102206 China

ISBN: (纸本)9780955529375

In this paper, genetic algorithm and modified dynamic programming are applied to path planning of robotic fish for the first time. Using grid method to the environment modeling and applying genetic algorithm to the path planning, an optimal or sub-optimal robot path can be obtained. Since the robotic fish can't track linear motion, the robot path can be seen as several circular arc. Based on the optimal path obtained via genetic algorithm, modified dynamic programming algorithm is proposed to calculate the shortest circular arc path, fish velocity and direction in every step. Finally the experiment on the robotic fish control software shows the effectiveness of the proposed method. © 2011 Intl Journal of Adv Mechatr.

关键词： Motion planning

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A new Robust Least Squares Support Vector Machine for Regression with Outliers

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Procedia Engineering 2011年 15卷 1355-1360页

作者： Lü You Liu Jizhen Qu Yaxin Key Laboratory of Measurement & Control New Technology and System for Industrial Process North China Electric Power University Beijing 102206 China

The least squares support vector machine (LS-SVM) is sensitive to noises or outliers. To address the drawback, a new robust least squares support vector machine (RLS-SVM) is introduced to solve the regression problem with outliers. A fuzzy membership function, which is determined by heuristic method, is assigned to each training sample as a weight. For each data point, firstly a deleted input neighborhood is found when the high-dimension feature space of input is focused on. Then the new field is reformulated after the output is brought in the neighborhood which we have found. The fuzzy membership function (weight) is set according to the distance from the data point to the center of its neighborhood and the radius of the neighborhood, which implies the probability to be an outlier. Two benchmark simulation experiments and analysis are presented to verify that the performance is improved.

关键词： Outlier Robust Weight Least squares support vector machine Regression

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Stochastic distribution control theory applied in superheated steam temperature systems

Stochastic distribution control theory applied in superheate...

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Chinese control Conference (CCC)

作者： Jianhua Zhang Fenfang Zhang Guolian Hou Jinfang Zhang Beijing Key Laboratory of Industrial Process Measurement and Control New Technology and System 102206 China

A novel control algorithm is applied to control superheated steam temperature in power plants. Since the disturbances existed in practical processes are probably non-Gaussian, the performance index is constructed by minimizing the entropy and mean value of tracking error besides the constraints on control energy. The optimal control solution is given and applied to control superheated steam temperature in a power plant. The simulation results verify its effectiveness.

关键词： Entropy Temperature control Process control Stochastic processes Temperature distribution Temperature measurement Performance analysis

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Stochastic distribution control theory applied in superheated steam temperature systems

Stochastic distribution control theory applied in superheate...

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作者： Zhang, Jianhua Zhang, Fenfang Hou, Guolian Zhang, Jinfang Beijing Key Laboratory of Industrial Process Measurement and Control New Technology and System Beijing 102206 China

ISBN: (纸本)9789881725592

关键词： Probability density function

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A study on the application of BPNN based on Minimum Error Entropy in electricity price forecasting

A study on the application of BPNN based on Minimum Error En...

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Jianhua Zhang Fang Wang Rui Wang Guolian Hou Beijing Key Laboratory of Industrial Process Measurement & Control New Technology and System North China Electric Power University Beijing China

Market clearing price (MCP) forecasting techniques is very important for the development of the electricity market. A three-layered neural network is used to predict electricity prices. MCP is seen as a multi-input single-output system and the historical electricity price and load data is utilized in an electricity market. The neural network is based on Minimum Entropy Error (MEE) cost function and Batch-Sequential mode. Compared with other models, the proposed approach improves the prediction accuracy and speed.

关键词： Electricity Forecasting Artificial neural networks Entropy Predictive models Accuracy Training

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Neural based PID control for networked processes

Neural based PID control for networked processes

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作者： Chen, J. Cheng, N. Zhang, J. Department of Chemical Engineering Chung-Yuan Christian University Chung-Li 320 Taiwan National Institute of Standards and Technology Boulder CO United States Beijing Key Laboratory of Industrial Process Measurement and Control New Technology and System North China Electric Power University Beijing 102206 China

A neural based PID feedback control method for networked process control systems is presented. As there are some uncertain factors such as external disturbance, randomly delayed measurements or control demands in real networked process control systems, the proposed PID controller is implemented by backpropagation neural networks whose weights are updated via minimizing tracking error entropy of closed loop systems. To demonstrate the potential applications of the proposed strategy, an example of a simulated batch reactor is provided. The proposed design method is shown to be useful and effective in dealing with network process control systems. © 2011 Zhejiang University.

关键词： Batch reactors

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Path planning of robotic fish based on genetic algorithm and modified dynamic programming

Path planning of robotic fish based on genetic algorithm and...

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International Conference on Advanced Mechatronic systems (ICAMechS)

作者： Qian Yang Mei Yu Shu Liu Zhong-ming Chai School of Control and Computer Engineering Electric Power University Beijing P. R. China Key Laboratory of Measurement and Control New Technology and System for Industrial Process North China Electric Power University Beijing P. R. China

关键词： Marine animals Robot kinematics Genetic algorithms Turning Path planning Dynamic programming

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Research on consensus control for a class of high-order multi-agent systems

Research on consensus control for a class of high-order mult...

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作者： Zhang, Wenguang Guo, Zhenkai Beijing Key Laboratory of Measurement and Control New Technology and System for Industrial Process School of Control and Computer Engineering North China Electric Power University Beijing 102206 China School of Mathematics and Information Ludong University Yantai 264205 China

ISBN: (纸本)9789881725592

This paper researches the consensus problem of high-order multi-agent systems. A new dynamic neighbor-based protocol is proposed which contains two parts, one is the local feedback and the other is the distributed feedback of the first states of each agent. Sufficient conditions are derived for state consensus of the system. Finally, simulation results are shown the effectiveness of the obtained results. © 2011 Chinese Assoc of Automati.

关键词： Multi agent systems

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Neural based PID control for networked processes

Neural based PID control for networked processes

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International Symposium on Advanced control of Industrial Processes (ADCONIP)

作者： J. Chen N. Cheng J. Zhang Department of Chemical Engineering Chung Yuan Christian University Chungli Taiwan Beijing Key Laboratory of Industrial Process Measurement & Control New Technology and System North China Electric Power University Beijing China National Institute for Standards and Technology Boulder CO USA

关键词： Process control Delay Artificial neural networks control systems Stochastic processes Inductors Entropy

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