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World Congress on Intelligent control and Automation (WCICA)

作者： Xuan Guo Wenli Du Rongbin Qi Feng Qian Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education 200237 China

A double-layer optimization algorithm (DLOA) was proposed to solve the minimum time dynamic optimization problem. The first step of DLOA was to discrete time region and control region. The inner optimization is to construct optimal control problem with free final states. Differential evolution algorithm is used to find the optimal solution in given terminal time, then the optimization results was compared with the threshold set. In the outer, DLOA calculated the time range of next iteration according to the inner calculation. When applied to typical minimum time dynamic optimization problem, DLOA demonstrated a competitive optimal searching ability and more accurate optimization results. DLOA could solve the optimization problem with local optimum and applied to models without gradient information.

关键词： optimization Heuristic algorithms chemical engineering Computers Educational institutions chemicals Dynamic programming

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Multiple models adaptive control based on cluster-optimization for a class of nonlinear system

Multiple models adaptive control based on cluster-optimizati...

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10th World Congress on Intelligent control and Automation, WCICA 2012

作者： Huang, Miao Wang, Zhenlei Qian, Feng Wang, Xin Key Laboratory of Advanced Control and Optimization for Chemical Processes East China University of Science and Technology Shanghai 200237 China Center of Electrical and Electronic Technology Shanghai Jiao Tong University Shanghai 200237 China

ISBN: (纸本)9781467313988

For a class of nonlinear discrete time system with fast time-varying or jumping parameters, a multiple models adaptive controller (MMAC) based on cluster-optimization is proposed. Based on the input-output data, the sample data are classified into several clusters by the fuzzy kernel clustering adaptive algorithm. Then the local models can be constructed corresponding clusters by the least square method. To improve the transient response during the change of the working points, besides the distance, the directional derivative of system is computed also. It is utilized to identify the system trend of changing working point. Before the changing occurs, new weighted models are developed by the corresponding local models, indicated by the system directional derivative. Meanwhile the distance between the data and the centre of clusters are used to find the weighted coefficients. So a better approach ability can be got than that designed only by the distance. The simulation results show that the proposed controller is superior to that of the conventional multiple models controller. © 2012 IEEE.

关键词： Transient analysis

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FRDPC subspace construction integrated with Bayesian inference for efficient monitoring of dynamic chemical processes 36

FRDPC subspace construction integrated with Bayesian inferen...

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第36届中国控制会议

作者： Yang Wang Qingchao Jiang Jingqi Fu School of Mechatronic Engineering and Automation Shanghai University School of Electric Engineering Shanghai Dianji University Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education East China University of Science and Technology

ISBN: (纸本)9781538629185

Modern chemical processes are usually characterized by large-scale,complex correlation,and strong dynamics,and monitoring of such processes is *** paper proposes a performance-driven fault-relevant dynamic principal component(FRDPC) subspace construction integrated with Bayesian inference method to achieve efficient monitoring for dynamic chemical ***,dynamic principal component analysis is employed to deal with both auto-correlation and cross-correlation among ***,considering fault information has no definite mapping to a certain dynamic principal component(DPC) and the existence of non-beneficial DPCs may cause redundancy in the monitoring,an FRDPC subspace is constructed for each fault through the performance-driven DPC *** new process measurements are examined in each FRDPC subspace as well as the residual *** monitoring results in all subspaces are fused to a comprehensive index through Bayesian inference to provide an intuitive indication of the process *** studies on a numerical example and the Tennessee Eastman benchmark process indicate the efficiency.

关键词： fault detection dynamic principal component analysis Bayesian inference dynamic chemical processes

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Design and control of a p-xylene oxidation process

Design and control of a p-xylene oxidation process

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作者： Tao, Lili Hu, Zhihua Qian, Feng College of Engineering Shanghai Second Polytechnic University Shanghai201209 China Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai200237 China

The p-xylene (PX) oxidation process is of great industrial importance because of the strong demand of the global polyester fiber. A steady-state model of the PX oxidation has been studied by many researchers. In our previous work, a novel industrial p-xylene oxidation reactor model using the free radical mechanism based kinetics has been developed. However, the disturbances such as production rate change, feed composition variability and reactor temperature changes widely exist in the industry process. In this paper, dynamic simulation of the PX oxidation reactor was designed by Aspen Dynamics and used to develop an effective plantwide control structure, which was capable of effectively handling the disturbances in the load and the temperature of the reactor. Step responses of the control structure to the disturbances were shown and served as the foundation of the smooth operation and advanced control strategy of this process in our future work. © 2015 Elsevier B.V.

关键词： Oxidation

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A framework for high-dimensional robust evolutionary multi-objective optimization 18

A framework for high-dimensional robust evolutionary multi-o...

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2018 Genetic and Evolutionary Computation Conference, GECCO 2018

作者： Du, Wei Tong, Le Tang, Yang Key Laboratory of Advanced Control Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai China College of Information Mechanical and Electrical Engineering Shanghai Normal University Shanghai China

ISBN: (纸本)9781450357647

This paper proposes a framework for solving high-dimensional robust multi-objective optimization problems. A decision variable classification-based framework is developed to search for robust Pareto-optimal solutions. The decision variables are classified as highly and weakly robustness-related variables based on their contributions to the robustness of candidate solutions. In the case study, an order scheduling problem in the apparel industry is investigated via the proposed framework. The experimental results reveal that the performance of robust evolutionary optimization can be greatly improved via analyzing the properties of decision variables and then decomposing the high-dimensional robust multiobjective optimization problem. © 2018 Association for Computing Machinery.

关键词： Evolutionary algorithms

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Optimal control nodes selection for consensus in multi-agent systems 19

Optimal control nodes selection for consensus in multi-agent...

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19th IFAC World Congress on International Federation of Automatic control, IFAC 2014

作者： Yang, Wen Wang, Xiaofan Shi, Hongbo Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai200237 China Department of Automation Shanghai Jiao Tong University Shanghai200240 China

ISBN: (纸本)9783902823625

In a resource limited multi-agent system, it is of practical importance to select a fraction of nodes (agents) to provide control inputs such that consensus can be achieved with optimized performance in terms of network cost and/or convergence speed. In this paper, we investigate the problem of how to select control nodes so as to minimize the network cost, where the control nodes are selected at the beginning and will be fixed all the time. This problem can be transformed to a combinatorial optimization problem, and further relaxed to a convex optimization problem with reweighted l1 norm. We propose a suboptimal algorithm to solve the convex optimization problem. Finally, we offer several numerical examples to illustrate the efficiency of the proposed strategies, and investigate the relationship how the degrees of control nodes will influence network cost and convergence speed. © IFAC.

关键词： Multi agent systems

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Automatic prediction of sleep level during day time short nap

Automatic prediction of sleep level during day time short na...

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IEEE/Institute of Complex Medical Engineering International Conference on Complex Medical Engineering

作者： Wang, Bei Fu, Xiuyan Wang, Xingyu Nakamura, Masatoshi Key Laboratory of Advanced Control and Optimization for Chemical Processes School of Information Science and Technology East China University of Science and Technology Shanghai China Research Institute of Systems Control Institute for Advanced Research and Education Saga University Saga Japan

ISBN: (纸本)9781424493241

The day time nap sleep has the significant prophylactic function to our health and working efficiency. In this study, the prediction of sleep level for day time short nap was investigated. The ultimate purpose was to develop the automatic analysis technique for nap sleep interpretation. Firstly, the conditional probabilities of sleep stage awake, stage 1 and stage 2 were calculated for the consecutive data segments based on a set of parameters. Secondly, the sleep level of next data segment was predicted according to the obtained conditional probabilities. The prediction of sleep level reflected the future sleep tendency and was helpful for automatic nap sleep interpretation. © 2011 IEEE.

关键词： Forecasting

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Gaussian process assisted coevolutionary estimation of distribution algorithm for computationally expensive problems

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Journal of Central South University 2012年第2期19卷 443-452页

作者： Luo, Na Qian, Feng Zhao, Liang Zhong, Wei-min Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education East China University of Science and Technology Shanghai China

In order to reduce the computation of complex problems, a new surrogate-assisted estimation of distribution algorithm with Gaussian process was proposed. Coevolution was used in dual populations which evolved in parallel. The search space was projected into multiple subspaces and searched by sub-populations. Also, the whole space was exploited by the other population which exchanges information with the sub-populations. In order to make the evolutionary course efficient, multivariate Gaussian model and Gaussian mixture model were used in both populations separately to estimate the distribution of individuals and reproduce new generations. For the surrogate model, Gaussian process was combined with the algorithm which predicted variance of the predictions. The results on six benchmark functions show that the new algorithm performs better than other surrogate-model based algorithms and the computation complexity is only 10% of the original estimation of distribution algorithm.

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Automatic spike detection based on real-time multi-channel template

Automatic spike detection based on real-time multi-channel t...

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2011 4th International Conference on Biomedical Engineering and Informatics, BMEI 2011

作者： Ji, Zhanfeng Wang, Xingyu Sugi, Takenao Goto, Satoru Nakamura, Masatoshi Key Laboratory of Advanced Control and Optimization for Chemical Processes East China University of Science and Technology Ministry of Education Shanghai China Department of Advanced Systems Control Engineering Saga University Saga Japan Research Institute of Systems Control Saga University Saga Japan

ISBN: (纸本)9781424493524

Automatic EEG spike detection provide valuable information for diagnosis of epilepsy. In the past 30 years, a number of algorithms were proposed. However, the basic idea of most algorithms is to identify spike activities. We have developed an algorithm based on identify non-spike activities for off-line analysis. In this paper, we improved the algorithm for online application using real-time template. Template method is a typical spike detection method. It was not widely employed because of the difficulty of making template. Our results suggested that multi-channel template is meaningful and effective for real-time detection. Spike activities can be detected with a delay of about 2 s. We also designed an simple user-friendly monitor interface for monitor and review spike events in real-time. © 2011 IEEE.

关键词： multi-channel template real-time spike detection Spike spike spikes jigs(templates) Real time algorithms MONITOR analysis of flowline

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Quantum clustering-based weighted linear programming support vector regression for multivariable nonlinear problem

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Soft Computing 2009年第9期14卷 921-929页

作者： Yu, Yanfang Qian, Feng Liu, Huimin Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai China

Linear programming support vector regression shows improved reliability and generates sparse solution, compared with standard support vector regression. We present the v-linear programming support vector regression approach based on quantum clustering and weighted strategy to solve the multivariable nonlinear regression problem. First, the method applied quantum clustering to variable selection, introduced inertia weight, and took prediction precision of v-linear programming support vector regression as evaluation criteria, which effectively removed redundancy feature attributes and also reduced prediction error and support vectors. Second, it proposed a new weighted strategy due to each data point having different influence on regression model and determined the weighted parameter p in terms of distribution of training error, which greatly improved the generalization approximate ability. Experimental results demonstrated that the proposed algorithm enabled the mean squared error of test sets of Boston housing, Bodyfat, Santa dataset to, respectively, decrease by 23.18, 78.52, and 41.39%, and also made support vectors degrade rapidly, relative to the original v-linear programming support vector regression method. In contrast with other methods exhibited in the relevant literatures, the present algorithm achieved better generalization performance.

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