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Chinese control Conference (CCC)

作者： Hao Zhang Gang Feng Huaicheng Yan Qijun Chen Department of Control Science and Engineering Tongji University Shanghai 200092 China Department of Mechanical & Biomedical Engineering City University of Hong Kong Hong Kong Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education School of Information Science and Engineering East China University of Science and Technology Shanghai 200237 China

In this paper, the synchronization of a nonlinear network with time-varying coupling delay is investigated via distributed impulsive control. Our objective is to design the distributed impulsive controller with minimum coupling strength such that the nonlinear network with coupling delay is globally exponentially synchronous. Some sufficient conditions have been derived based on Lyapunov-Razumikhin method in terms of matrix inequalities. An example is presented to illustrate the effectiveness of the proposed control methods.

关键词： Couplings Synchronization Oscillators Linear matrix inequalities Delays Network topology Topology

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Denoising and Rhythms Extraction of EEG under +Gz Acceleration Based on Wavelet Packet Transform

Denoising and Rhythms Extraction of EEG under +Gz Accelerati...

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2013 ICME International Conference on Complex Medical Engineering(2013 ICME复合医学工程国际会议)

作者： Yifeng Li Tao Zhang Lue Deng Bei Wang Masatoshi Nakamura Department of Automation Tsinghua University Beijing 100084 China Air Force Institute of Aeromed Department of Automation Tsinghua University Beijing 100084 China Key Laboratory of Advanced Con Air Force Institute of Aeromedicine Beijing 100142 China Key Laboratory of Advanced Control and Optimization for Chemical Processes Shanghai 200237 China Research Institute of Systems Control Saga University Saga 840-0047 Japan

ISBN: (纸本)9781467329705

Filtering and feature extraction are very important in the analysis and study of EEG signal under +Gz *** this study,a new filter of different frequency characteristics of EEG signal under +Gz acceleration is constructed and four kinds of rhythms of EEG signal are extracted by using wavelet packet *** under different G loads is analyzed and compared,and then EEG dynamic characteristics are studied in order to analyze its advantages and *** results show that wavelet packet method can effectively suppress interference bands in EEG,such as EMG,power and so on,and effectively reflect the dynamic characteristics of different rhythms,exhibiting good *** proposed method is also applicable for analyzing and studying other dynamic biomedical signals.

关键词： EEG wavelet packet filtering denoising rhythms extraction

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Multimode Process Monitoring Based on Fuzzy C-means in Locality Preserving Projection Subspace

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Chinese Journal of chemical Engineering 2012年第6期20卷 1174-1179页

作者：解翔侍洪波 Key Laboratory of Advanced Control and Optimization for Chemical Processes East China University of Science and TechnologyMinistry of Education

For complex industrial processes with multiple operational conditions, it is important to develop effective monitoring algorithms to ensure the safety of production processes. This paper proposes a novel monitoring strategy based on fuzzy C-means. The high dimensional historical data are transferred to a low dimensional subspace spanned by locality preserving projection. Then the scores in the novel subspace are classified into several overlapped clusters, each representing an operational mode. The distance statistics of each cluster are integrated though the membership values into a novel BID (Bayesian inference distance) monitoring index. The efficiency and effectiveness of the proposed method are validated though the Tennessee Eastman benchmark process.

关键词： multimode process monitoring fuzzy C-means locality preserving projection integrated monitoring index Tennessee Eastman process

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Soft Sensor for Ammonia Concentration at the Ammonia Converter Outlet Based on an Improved Group Search optimization and BP Neural Network

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Chinese Journal of chemical Engineering 2012年第6期20卷 1184-1190页

作者：阎兴頔杨文马贺贺侍洪波 Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education East China University of Science and Technology

The ammonia synthesis reactor is the core unit in the whole ammonia synthesis production. The ammonia concentration at the ammonia converter outlet is a significant process variable, which reflects directly the production efficiency. However, it is hard to be measured reliably online in real applications. In this paper, a soft sensor based on BP neural network (BPNN) is applied to estimate the ammonia concentration. A modified group search optimization with nearest neighborhood (GSO-NH) is proposed to optimize the weights and thresholds of BPNN. GSO-NH is integrated with BPNN to build a soft sensor model. Finally, the soft sensor model based on BPNN and GSO-NH (GSO-NH-NN) is used to infer the outlet ammonia concentration in a real-world application. Three other modeling methods are applied for comparison with GSO-NH-NN. The results show that the soft sensor based on GSO-NH-NN has a good prediction performance with high accuracy. Moreover, the GSO-NH-NN also provides good generalization ability to other modeling problems in ammonia synthesis production.

关键词： ammonia synthesis ammonia concentration soft sensor group search optimization

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Peak Traffic Forecasting Using Nonparametric Approaches

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上海交通大学学报（英文版） 2012年第1期17卷 76-81页

作者： ZHANG Yang WANG Meng-ling Shanghai Municipal Transportation Information Center Shanghai 200032 China Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education East China University of Science and Technology Shanghai 200237 China

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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2012 8th International Conference on Natural Computation, ICNC 2012

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

ISBN: (纸本)9781457721311

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. © 2012 IEEE.

关键词： Vectors

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Prediction of Cracking Gas Compressor Performance and Its Application in Process optimization

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Chinese Journal of chemical Engineering 2012年第6期20卷 1089-1093页

作者：李绍军李凤 Key Laboratory of Advanced Control and Optimization for Chemical Processes(East China University of Science and Technology) Ministry of Education

Cracking gas compressor is usually a centrifugal compressor. The information on the performance of a centrifugal compressor under all conditions is not available, which restricts the operation optimization for compressor. To solve this problem, two back propagation (BP) neural networks were introduced to model the performance of a compressor by using the data provided by manufacturer. The input data of the model under other conditions should be corrected according to the similarity theory. The method was used to optimize the system of a cracking gas compressor by embedding the 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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An improved particle swarm algorithm based on cultural algorithm for constrained optimization

An improved particle swarm algorithm based on cultural algor...

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作者： Wang, Lina Cao, Cuiwen Xu, Zhenhao Gu, Xingsheng Key Laboratory of Advanced Control and Optimization for Chemical Processes East China University of Science and Technology Ministry of Education Shanghai 200237 China

ISBN: (纸本)9783642277078

This paper develops an improved particle swarm optimization algorithm based on cultural algorithm for constrained optimization problems. Firstly, chaos method is utilized in the initialization process of single swarm in population space to assure the searching breadth, and evolves with standard particle swarm optimization (PSO). Secondly, fixed proportion elites are selected from population space to construct the swarm of belief space through acceptance function. Then, the belief space updates its normative knowledge and situational knowledge according to the elite particles, and the elite-swarm in the belief space performs PSO operation according to the update knowledge and generates new particles. After that, the belief space renews the knowledge again, and passes down the new knowledge which has been updated twice to give better guidance to all the particles in the population space. The efficiency of the initialization strategy and the double evolving knowledge strategy are verified in six constrained optimization problems. © 2012 Springer-Verlag GmbH Berlin Heidelberg.

关键词： Constrained optimization

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Process monitoring of multimode processes using kernel independent component analysis

Process monitoring of multimode processes using kernel indep...

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8th International Symposium on Advanced control of chemical processes, ADCHEM 2012

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

ISBN: (纸本)9783902823052

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. © 2012 IFAC.

关键词： Process monitoring

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Soft sensor for ammonia concentration at the ammonia converter outlet based on an improved group search optimization and BP neural network

Soft sensor for ammonia concentration at the ammonia convert...

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第23届过程控制会议

作者： Xingdi Yan Wen Yang Hehe Ma Hongbo Shi Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education

The ammonia synthesis section is the core during the whole ammonia synthesis production. The ammonia concentration at the ammonia converter outlet is a significant process variable, which reflects the production efficiency directly. However, it is hard to be measured reliably online in real applications. In this paper, a soft sensor based on BP neural network (BPNN) is applied to estimate the ammonia concentration. A modified group search optimization with nearest neighborhood (GSO-NH) is proposed to optimize the weights and thresholds of BPNN. GSO-NH is integrated with BPNN to build a soft sensor model. Finally, the soft sensor model based on BPNN and GSO-NH (GSO-NH-NN) is used to infer the outlet ammonia concentration in a real-world application. Three other modeling methods are applied to compare with GSO-NH-NN. The results show that the soft sensor based on GSO-NH-NN has a good prediction performance with high accuracy. Moreover, the GSO-NH-NN also provides good generalization ability to other modeling problems in ammonia synthesis production.

关键词： Ammonia synthesis Ammonia concentration Soft sensor Group search optimization.

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