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Multiple Model Soft Sensor Based on Affinity Propagation, Gaussian Process and Bayesian Committee Machine

Chinese Journal of Chemical Engineering 2009年第1期17卷 95-99页

作者：李修亮苏宏业褚健 National Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang Uni- versity Hangzhou 310027 China

Presented is a multiple model soft sensing method based on Affinity Propagation （AP）, Gaussian process （GP） and Bayesian committee machine （BCM）. AP clustering arithmetic is used to cluster training samples according to their operating points. Then, the sub-models are estimated by Gaussian Process Regression （GPR）. Finally, in order to get a global probabilistic prediction, Bayesian committee mactnne is used to combine the outputs of the sub-estimators. The proposed method has been applied to predict the light naphtha end point in hydrocracker fractionators. Practical applications indicate that it is useful for the online prediction of quality monitoring in chemical processes.

关键词： multiple model soft sensor affinity propagation Gaussian process Bayesian committee machine

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Modeling of Isomerization of C_8 Aromatics by Online Least Squares Support Vector Machine

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Chinese Journal of Chemical Engineering 2009年第3期17卷 437-444页

作者：李丽娟苏宏业褚建 College of Automation and Electrical Engineering Nanjing University of Technology State Key Lab. of Industrial Control Technology Institute of Cyber-systems and ControlZhejiang University

The least squares support vector regression (LS-SVR) is usually used for the modeling of single output system, but it is not well suitable for the actual multi-input-multi-output system. The paper aims at the modeling of multi-output systems by LS-SVR. The multi-output LS-SVR is derived in detail. To avoid the inversion of large matrix, the recursive algorithm of the parameters is given, which makes the online algorithm of LS-SVR practical. Since the computing time increases with the number of training samples, the sparseness is studied based on the pro-jection of online LS-SVR. The residual of projection less than a threshold is omitted, so that a lot of samples are kept out of the training set and the sparseness is obtained. The standard LS-SVR, nonsparse online LS-SVR and sparse online LS-SVR with different threshold are used for modeling the isomerization of C8 aromatics. The root-mean-square-error (RMSE), number of support vectors and running time of three algorithms are compared and the result indicates that the performance of sparse online LS-SVR is more favorable.

关键词： least squares support vector machine multi-variable online sparseness isomerization

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Survey on Computational Complexity with Phase Transitions and Extremal Optimization

Survey on Computational Complexity with Phase Transitions an...

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Joint 48th IEEE Conference on Decision and control and 28th Chinese control Conference

作者： Guo-Qiang Zeng Yong-Zai Lu State Key Laboratory of Industrial Control Technology & Institute of Cyber Systems and Control Zhejiang University IEEE

Applying statistical mechanics to search problems in AI, decisions and optimization has been one of the powerful channels to solve NP-hard problems. Extensive analytical and experimental research has shown that the "phase transition" phenomenon in search space is often associated with the hardness of complexity. A Bak-Sneppen(BS) model based general-purpose heuristic method, called extremal optimization(EO), proposed by Boettcher and Percus from physics society may perform very well, especially near the phase transitions in compared with other optimization methods, e.g., genetic algorithm and simulated annealing, etc. To actuate more extensive investigations on this new optimization approach particularly in control, computer and optimization communities,this survey reviews the latest research results from fundamental to practice about the connection between computational complexity and phase transitions. Then, further introduces the concepts, fundamentals, algorithms and applications of EO from its capability of self-organized criticality, backbone analysis and co-evolution moving to a far-from-equilibrium state. Finally, the concluding remarks with suggested future research are illustrated.

关键词： Survey on Computational Complexity with Phase Transitions and Extremal Optimization

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Optimizing Crude Oil Operations under Uncertainty

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IFAC Proceedings Volumes 2009年第4期42卷 1020-1025页

作者： Jishuai Wang Yiping Feng Gang Rong State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou 310027 P. R. China

This paper presents a novel approach for refinery crude oil operations under uncertainty. Due to the flexibility of the crude oil scheduling, decisions made by deterministic optimizations are often conservative or lack of robustness or even infeasible, so in this paper future uncertainties are considered to improve feasibility and robustness of the schedule. To handle fluctuating product demand and uncertain ship arrival time, deterministic formulation is replaced by chance constrained programming. Through a series of examples, it proves that by using probabilistic programming, the solution of the problem provides a more robust scheduling under a comprehensive confidence level. The relationship between the probability and reliability of a planned operation is also discussed.

关键词： Crude oil operations scheduling optimization model stochastic programming uncertainty

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A New Algorithm for Mining Global Frequent Itemsets in a Stream

A New Algorithm for Mining Global Frequent Itemsets in a Str...

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International Conference on Fuzzy systems and Knowledge Discovery (FSKD)

作者： Lichao Guo Hongye Su Yu Qu State Key Laboratory Of Industrial Control Technology Institute of Cyber-Systems and Control University of Zhejiang Hangzhou China

To find global frequent itemsets in a multiple, continuous, rapid and time-varying data stream, a fast, incremental, real-time, and little-memory-cost algorithm should be used. Based on the max-frequency window model, a BHS summary structure and a novel algorithm called GGFI-MFW are proposed. It merely updates the summaries for subsets of the data new arrived and could directly generate the max-frequency for a given item set without scanning the whole summary. Experiment results indicate that the proposed algorithm could efficiently find global frequent itemsets over a data stream with a small memory and perform overwhelming superiority for a large number of distinct items.

关键词： Data mining Itemsets Marketing and sales Fuzzy systems industrial control Ice Frequency measurement Frequency shift keying Research and development

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A Support Vector Regression Approach for Recursive Simultaneous Data Reconciliation and Gross Error Detection in Nonlinear Dynamical systems

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Acta Automatica Sinica 2009年第6期35卷 707-716页

作者： Yu MIAO Hong-Ye SU Jian CHU National Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou 310027 P.R. China

The quality of process data in a chemical plant significantly affects the performance and benefits gained from activities like performance monitoring, online optimization, and control. Since many chemical processes often show nonlinear dynamics, techniques like extended Kalman filter (EKF) and nonlinear dynamic data reconciliation (NDDR) have been developed to improve the data quality. Recently, the recursive nonlinear dynamic data reconciliation (RNDDR) technique has been proposed, which combines the merits of EKF and NDDR techniques. However, the RNDDR technique cannot handle measurements with gross errors. In this paper, a support vector (SV) regression approach for recursive simultaneous data reconciliation and gross error detection in nonlinear dynamical systems is proposed. SV regression is a compromise between the empirical risk and the model complexity, and for data reconciliation it is robust to random and gross errors. By minimizing the regularized risk instead of the maximum likelihood in the RNDDR, our approach could achieve not only recursive nonlinear dynamic data reconciliation but also gross error detection simultaneously. The nonlinear dynamic system simulation results in this paper show that the proposed approach is robust, efficient, stable, and accurate for simultaneous data reconciliation and gross error detection in nonlinear dynamic systems within a recursive real-time estimation framework. It can also give better performance of control.

关键词： Support vector regression data reconciliation gross error detection nonlinear estimation

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Multi-step prediction error approach for MPC performance monitoring

Multi-step prediction error approach for MPC performance mon...

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作者： Zhao, Yu Chu, Jian Su, Hongye Huang, Biao State Key Lab. of Industrial Control Technology Institute of Cyber Systems and Control Zhejiang University Hangzhou China Department of Chemical and Materials Engineering University of Alberta Edmonton AB T6G 2G6 Canada

ISBN: (纸本)9783902661548

Performance monitoring of model predictive control systems (MPC) has received a great interest from both academia and industry. In recent years some novel approaches for multivariate control performance monitoring have been developed without the requirement of process models or interactor matrices. Among them the prediction error approach has been shown to be a promising one, but it is k-step prediction based and may not be fully comparable with the MPC objective that is multi-step prediction based. This paper develops a multi-step prediction error approach for performance monitoring of model predictive control systems, and demonstrates its application in an industrial MPC performance monitoring and diagnosis problem.

关键词： Model predictive control

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On Separation Principle for a Class of Networked control systems

On Separation Principle for a Class of Networked Control Sys...

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Joint 48th IEEE Conference on Decision and control and 28th Chinese control Conference

作者： Dongxiao Wu Jun Wu Sheng Chen State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and ControlZhejiang University School of Electronics and Computer Science University of Southampton

In this contribution, we investigate a class of observer-based discrete-time networked control systems(NCSs)with random packet dropouts occurring independently in both the sensor-to-controller(S/C) and controller-to-actuator(C/A)*** first propose and prove a separation principle for the general class of NCSs where packet dropouts in the C/A and S/C channels are governed by two independent Markov chains, *** then derive necessary and sufficient conditions, in terms of linear matrix inequalities, for synthesis of stabilisation control of a class of NCSs where the C/A channel is driven by a Markov chain while the S/C channel is driven by a Bernoulli process.A numerical example is provided to illustrate the effectiveness of our proposed method.

关键词： NCS dropout On Separation Principle for a Class of Networked control systems

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Robust D Stabilization of Singular systems with Polytopic Uncertainties

Robust D Stabilization of Singular Systems with Polytopic Un...

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2009 American control Conference (ACC 2009)

作者： Jianjun Bai Hongye Su State Key Laboratory of Industrial Control Technology Institute of Cyber-System and Control Zhejiang University 310027 Hangzhou China

ISBN: (纸本)9781424445233

The robust D stabilization problem is considered for singular systems with polytopic uncertainties in this paper. Both the derivative matrix E and the state matrix A are with uncertainties, under the assumption that the rank of matrix E is constant. Firstly, with the introduction of some free matrices, a new dilated LMI condition for the singular system to be D stable is proposed, based on which, the robust D stable problem is solved, and a sufficient condition for the closed system to be robust D stabilizable is obtained. The desired state feedback controller is given in an explicit expression. Numerical examples show the efficiency of the obtained approach.

关键词： Singular system deuterium ABSORBED DOSE state feedback controller Uncertainty rank of matrix matrices

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Multivariable control Performance Assessment Based On Generalized Minimum Variance Benchmark

Multivariable Control Performance Assessment Based On Genera...

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Joint 48th IEEE Conference on Decision and control and 28th Chinese control Conference

作者： Yu Zhao Hongye Su Jian Chu Chao Zhao Dengfeng Zhang State Key Lab.of Industrial Control Technology Institute of Cyber-Systems and ControlZhejiang University School of Mechanical Engineering Nanjing University of Science and Technology

This paper is concerned with the control performance assessment based on the multivariable generalized minimum variance *** an explicit expression for the feedback controller-invariant(here we call it generalized minimum variance) term of the multivariable control systems is obtained, and consequently the derived feedback controllerinvariant term is used as a standard benchmark for the assessment of the control performance of MIMO *** proposed approach is based on the multivariable minimum variance benchmark and univariate generalized minimum variance *** is the extension of the above two *** comparison with the minimum variance benchmark, the new approach is more reasonable and practical for the control performance assessment of multivariable ***, it does need more information of the *** utility of the developed approach is illustrated by simulation example.

关键词： Multivariable control Performance Assessment Based On Generalized Minimum Variance Benchmark

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