检索结果-内蒙古大学图书馆

第23届过程控制会议

作者： Shaojun Li Feng Li Key Laboratory of Advanced Control and Optimization for Chemical Processes (East China University of

In ethylene plant, charge gas compressor is one of the most important units. The charge gas compressor usually is a centrifugal compressor. The information of centrifugal compressors’ performance is not applicable to all of the conditions, which restricts the operation optimization of the compressor. To solve this problem, a tri-layer BP neural network was introduced to model the performance of compressor by using the data provided by manufacturers. The input data of the model in other conditions should be corrected according to the similar theory. At last, the method was used to optimize the system of charge gas compressor by embedding compressor performance model into the ASPEN PLUS model of compressor. The result shows that it is an effective method to optimize the compressor system.

关键词： Compressor characteristic curve neural-network modeling

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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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Minimum time dynamic optimization using double-layer optimization algorithm

Minimum time dynamic optimization using double-layer optimiz...

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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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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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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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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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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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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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Process Monitoring of Multimode processes Using Kernel Independent Component Analysis

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IFAC Proceedings Volumes 2012年第15期45卷 697-701页

作者： Yingwei Zhang Jiayu An Chi Ma State Key Laboratory of Synthetical Automation of Process Industry Northeastern University Shenyang Liaoning 110819 PR China Key Laboratory of Advanced Control and Optimization for Chemical Processes Shanghai 200237 PR China

In the paper, a new process monitoring approach is proposed for handling the multimode problem in the industrial processes. The original space can be separated into two different parts, which are the common part and the specific part. There are both similarity and dissimilarity in the underlying correlations of different modes, which play different roles in the industrial processes. Because the industrial processes have the non-Gaussian and nonlinear characteristics, modified kernel independent component analysis is used to monitor the multimode processes in this paper. The global multimode basis vector and the multimode sub-basis vector are obtained based on the modified KICA. Then, the common part and specific part in one mode are respectively analyzed. The proposed method is applied to monitor the continuous annealing process. The proposed approach effectively captures the non-Gaussian and nonlinear relationship in different modes in the industrial processes.

关键词： Multimode process monitoring Common and specific correlations Kernel independent component analysis

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