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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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Event-trigged control for Discrete-time Multi-agent Networks

Event-trigged Control for Discrete-time Multi-agent Networks

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Asian control Conference

作者： Lulu Li Daniel W. C. Ho Yuanyuan Zou Chi Huang Jianquan Lu Department of Mathematics City University of Hong Kong Hong Kong. Key Laboratory of Advanced Control and Optimization for Chemical Process Ministry of Education East China University of Science and Technology Shanghai 200237 China Department of Mathematics Taiyuan University of Technology Taiyuan 030024 China. Department of Mathematics Southeast University Nanjing 210096 China.

ISBN: (纸本)9781467357661

In this paper, a new control strategy was proposed to deal with the discrete-time multi-agent consensus problem. Two types of protocols are discussed in this paper: i) networks of single-integrators without delay under centralized eventtriggered control and ii) networks of single-integrators with delay under distributed event-triggered control. For each consensus protocol, we prove that the multi-agent network will achieve consensus asymptotically. Numerical examples are provided to demonstrate the effectiveness of the obtained theoretical results.

关键词： Multi-agent networks consensus event-triggered control time delay

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Regularized CSP with Fisher's criterion to improve classification of single-trial ERPs for BCI

Regularized CSP with Fisher's criterion to improve classific...

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2012 9th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2012

作者： Zhang, Yu Zhao, Qibin Zhou, Guoxu Wang, Xingyu Cichocki, Andrzej Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai 200237 China Laboratory for Advanced Brain Signal Processing Brain Science Institute RIKEN Wako-shi Saitama 351-0198 Japan

ISBN: (纸本)9781467300223

A brain-computer interface (BCI) based on the combination of oddball paradigm and face perception has been introduced. Such BCI mainly exploits three event-related potential (ERP) components, namely vertex positive potential (VPP), N170 and P300 instead of only P300. With different temporal and spatial distributions of the three ERP components, a regularized common spatial pattern (CSP) with Fisher's criterion (FC), named FCCSP, is proposed to extract the most discriminative features for single trial classification of ERP components. With linear discriminant analysis (LDA) classifier, the proposed FCCSP spatial filtering method yields an average classification accuracy of 95.4% on seven healthy subjects for single-trial ERP components, which outperforms no spatial filtering, the CSP and the FC. © 2012 IEEE.

关键词： Brain computer interface

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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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Robust iterative learning control with quadratic performance index

Robust iterative learning control with quadratic performance...

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作者： Xu, Zuhua Zhao, Jun Yang, Yi Shao, Zhijiang Gao, Furong National Laboratory of Industrial Control Technology Department of Control Science and Engineering Zhejiang University Hangzhou 310027 China Key Laboratory of Advanced Control and Optimization for Chemical Processes Shanghai 200237 China Department of Chemical and Biomolecular Engineering Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong

In this paper, a robust iterative learning control (ILC) designed through a linear matrix inequality (LMI) approach is proposed first, based on the worst-case performance index with ellipsoidal uncertainty and polytopic uncertainty, respectively. Since the design based on worst-case performance index is too conservative, a novel ILC design based on nominal performance index is further proposed, and its robust convergence properties are proven. The latter can give better performance when the nominal model is close to the true process. Simulations have demonstrated the effectiveness and excellent performance of the proposed methods. © 2011 American chemical Society.

关键词： Iterative methods

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Modified Self-adaptive Immune Genetic Algorithm for optimization in the Combustion Side Reaction of p-Xylene Oxidation

Modified Self-adaptive Immune Genetic Algorithm for Optimiza...

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第23届过程控制会议

作者： Lili Tao Xiangdong Kong Weimin Zhong Feng Qian Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education E Automation Institute East China University of Science and Technology Shanghai 200237 China Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education E

In recent years, immune genetic algorithm (IGA) is gaining popularity for finding the optimal solution of non-linear optimization problems encountered in many engineering applications. In IGA, the mutation factor values are either fixed or change together according to a function of the individual’s current generation number during all the search process. However, IGA with deterministic mutation factor suffers from the problem of premature convergence. A modified self-adaptive immune genetic algorithm (MSIGA) with two memory bases, in which immune concepts are applied to determine the mutation parameters, is proposed to strengthen the searching ability of the algorithm and maintain population diversity. Performance comparisons with other well-known population-based iterative algorithms show that the proposed method can quickly converge to the global optimum and overcome premature problem. Then, this algorithm is applied to optimize a feed forward neural network to measure the content of products in the combust ion side reaction of p-xylene oxidation, and satisfactory results are obtained.

关键词： Self-adaptive immune genetic algorithm Artificial neural network Measurement p-Xylene oxidation process

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Peak Traffc Forecasting Using Nonparametric Approaches

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Journal of Shanghai Jiaotong university(Science) 2012年第1期17卷 76-81页

作者：张扬王梦灵 Shanghai Municipal Transportation Information Center Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education East China University of Science and Technology

States of traffic situations can be classified into peak and nonpeak periods. The complexity of peak traffic brings more difficulty to forecasting models. Travel time index （TTI） is a fundamental measure in transportation. How to master the characteristics and provide accurate real-time forecasts is essential to intelligent transportation systems （ITS）. Cooperating with state space approach, least squares support vector machines （LS- SVMs） are investigated to solve such a practical problem in this paper. To the best of our knowledge, it is the first time to apply the technique and analyze the forecast performance in the domain. For comparison purpose, other two nonparametric predictors are selected because of their effectiveness proved in past research. Having good generalization ability and guaranteeing global minima, LS-SVMs perform better than the others. Providing sufficient improvement in stability and robustness reveals that the approach is practically promising.

关键词： traffic forecasting peak traffic nonparametric techniques

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A Discrete Artificial Bee Colony Algorithm for Minimizing the Total Flow Time in the Blocking Flow Shop Scheduling Problem

A Discrete Artificial Bee Colony Algorithm for Minimizing th...

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第23届过程控制会议

作者： Guanlong Deng Zhenhao Xu Xingsheng Gu Key Laboratory of Advanced Control and Optimization for Chemical Process Ministry of Education Eas Key Laboratory of Advanced Control and Optimization for Chemical Process Ministry of Education Eas

A discrete artificial bee colony algorithm is proposed for solving the blocking flow shop scheduling problem with total flow time criterion. Firstly, the solution in the algorithm is represented as job permutation. Secondly, an initialization scheme based on a variant of the NEH heuristic and a local search is designed to construct the initial population with both quality and diversity. Thirdly, based on the idea of iterated greedy algorithm, some newly designed schemes for employed bee, onlooker bee and scout bee are presented. The performance of the proposed algorithm is tested on the well-known Taillard benchmark set, and computational results demonstrate the effectiveness of the discrete artificial bee colony algorithm. In addition, the best known solutions of the benchmark set are provided for the blocking flow shop scheduling problem with total flow time criterion.

关键词： Flow shop scheduling Blocking Artificial bee colony algorithm Total flow time

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Weighted least square support vector machine integrated with parameter optimization and its application in chemical process

Weighted least square support vector machine integrated with...

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International Conference on Natural Computation (ICNC)

作者： Wen-Tong Cui Xue-Feng Yan Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education East China University of Science and Technology Shanghai China

Considering that outliers can disrupt the correlation structure of least square support vector machine (LS-SVM), and that the parameters of LS-SVM play an important role in the performance, a novel weighted least square support vector machine integrated with parameter optimization is proposed to obtain the optimal parameters and to eliminate the effect of outliers. Several LS-SVM variants are applied in simulation experimentation and chemical process respectively to demonstrate the satisfactory performance of the proposed method.

关键词： optimization Training Support vector machines Oxidation Adaptation models Mathematical model Educational institutions

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Energy consumption monitoring of the steam pipe network based on affinity propagation clustering

Energy consumption monitoring of the steam pipe network base...

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World Congress on Intelligent control and Automation (WCICA)

作者： Xiazhu You Wenli Du Liang Zhao Feng Qian Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai 200237 China

The steam system is an important part of chemical utility system, but there are widespread phenomenon about lack of testing information, energy consumption configuration depend on given experience and wasting energy. So this paper puts forward a method about the steam pipe network system's status identification of different energy consumption based on the steam pipe network's characteristics of complex structure, much steam equipment, lack of testing information and difficult to build accurate mathematical model. The method based on affinity propagation clustering that can solve big set of data's clustering problem quickly and effective. As it is hard to find preference parameters and damping factor, this paper uses PSO to find the most optimal parameters in order to achieve the best clustering effect. This method is applied test both in classic data set and the steam pipe network of ethylene plant's status identification, the results show the effectiveness of this method.

关键词： Energy consumption Indexes optimization Clustering algorithms Temperature measurement Pattern recognition Frequency modulation

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