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检索条件"机构=The Key Laboratory of Advanced Control and Optimization for Chemical Process"

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The time series soft-sensor modeling based on Adaboost_LS-SVM

The time series soft-sensor modeling based on Adaboost_LS-SV...

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World Congress on Intelligent control and Automation (WCICA)

作者： W.-L. Du Z.-Q. Guan Feng Qian Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai China

With regards to the petrochemical processes with various operating states and dynamic performance which will affect estimation precision for the static soft sensor, a time series soft sensor model which uses the time series of process variables to estimate the dynamic performance of quality variable was proposed. Meanwhile, the integrated Adaboost learning algorithm is introduced. With the help of this method, training samples and modeling for several times, according to the modeling error to renew the next sample data, in order to obtain a series of different basic models. Every basic model will be weighted in the last step; as a result, a more precise combined LS-SVM model will be established. According to the prediction of benzene content of column reactor in the azeotropic rectification tower, the effectiveness of the method is demonstrated.

关键词： Support vector machines Time series analysis Data models chemical industry Estimation process control Heuristic algorithms

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Event-triggered model predictive control for wireless networked control systems with packet losses

Event-triggered model predictive control for wireless networ...

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IEEE International Conference on Cyber Technology in Automation, control, and Intelligent Systems

作者： Quan Wang Yuanyuan Zou Yugang Niu 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)9781479987313

This study investigates an event-triggered model predictive control for wireless networked control system with packet losses in the sensor-to-controller channel. Based on a predictive control compensation strategy, the closed-loop model with packet losses is established. The event-triggered conditions are derived by choosing the performance objective function of MPC as a Lyapunov function. Further, the maximal allowable number of successive packet losses is presented. Under the proposed mechanism, the energy consumption of the wireless network is alleviated and closed-loop stability is guaranteed. Finally, simulation results are shown to illustrate the effectiveness of the proposed method.

关键词： Packet loss Predictive control Networked control systems Wireless networks Closed loop systems Wireless sensor networks

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Dynamic optimization of chemical engineering problems using affinity propagation based estimation of distribution algorithm

Dynamic optimization of chemical engineering problems using ...

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World Congress on Intelligent control and Automation (WCICA)

作者： Na Luo Wei Feng Xiaoqiang Wang Feng Qian Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai China

Dynamic optimization has attracted much attention for its wide applications in engineering problems. However, it is still a challenge for high nonlinear, multi-dimensional and multimodal problems. Estimation of Distribution Algorithm was proposed in which probabilistic models extracted relevant features of the complex search space and then generated new individuals during optimization. In order to decrease the dependences among control variables in dynamic optimization, affinity propagation was applied to cluster the individuals in evolutionary iterations. In each cluster, the probabilistic density function of Gaussian mixture model refined the promising spaces with high quality solutions and avoided the random combination of different control variables. To evaluate the performance of the new approach, three dynamic optimization problems of chemical process are used as cases comparing with three state-of-the-art global optimization methods. The results showed that the new approach could achieve the best solution in most cases with less computational effort and higher efficiency.

关键词： Statistics Probabilistic logic optimization Sociology Algorithm design and analysis Robustness Genetic algorithms

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On-demand state estimation with sampling time skew in power systems

On-demand state estimation with sampling time skew in power ...

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Canadian Conference on Electrical and Computer Engineering (CCECE)

作者： Xinjun Ma Chensheng Liu Jing Wu Chengnian Long 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)9781479958306

With new dynamics and uncertainties in today's power grids, traditional fixed-interval State Estimation (SE) may be unable to track the variability and monitor the power grid effectively. This paper presents a new architecture to transfer data and execute SE on demand. A list of situations are summarized to direct the SE-demand generator in system control center. As SCADA and PMU measurements are co-exist in realistic power systems, time skew problem is inevitable. To mitigate the influence of time skew, a state estimator based on time skew oriented weight adaptation is considered. In each SE circle, the weights assigned to the measurements not only correspond to their noise, but also the time offsets relative to the SE-demand point. Numerical examples demonstrate the improved accuracy of our estimator compared with the conventional hybrid SE when measurements time skew is present.

关键词： Phasor measurement units Time measurement Measurement uncertainty Current measurement Computer architecture State estimation Uncertainty

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Containment control for the cooperative output regulation of linear multi-agent systems

Containment control for the cooperative output regulation of...

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Chinese control Conference (CCC)

作者： Yanchao Shen Huaicheng Yan Hao Zhang Hongbo Shi Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education Shanghai China Tongji University Shanghai Shanghai CN

In this paper, the containment control problem is studied for the cooperative output regulation of linear multi-agent systems(MASs). The output regulation problem initiated from single-leader-follower region is extended to the multi-leadersfollower region. The purpose of this paper is to design a containment control algorithm such that all the followers can move into the convex hull formed by the leaders. A sufficient and necessary condition in term of a linear matrix inequality (LMI) is derived for the existence of a containment controller protocol that guarantees the followers are capable to follow the trail of leader and the output error will converge to zero with the time elapsing. Then, a controller design procedure is given to construct the feedback gain matrix and select the proper gain matrix for achieving final consensus of the MASs. Compared with the existing algorithms, the proposed one requires less information and calculation of the velocities measurement of the agents. Finally, a numerical example is illustrated to show the usefulness of the designed containment controller.

关键词： Eigenvalues and eigenfunctions Educational institutions Lead Topology Laplace equations Multi-agent systems Algorithm design and analysis

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Multi-objective robust optimization based on NSGA-II and degree of robustness¯

Multi-objective robust optimization based on NSGA-II and deg...

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World Congress on Intelligent control and Automation (WCICA)

作者： Jie Qiang Rongbin Qi Feng Qian Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai China

This paper presents an approach to search robust optimal solutions with the concept of degree of robustness in multi-objective optimization problems. The definition of the degree of robustness is used in the calculation of the mean effective objective function(f eff (X)) and in the evolutionary process, degree of robustness is considered as a factor to select robust solutions which can meet the demand for better robustness. Some simulation results are shown to prove that this approach can control the distribution of objective value and help system improve robustness by finding solutions with better robustness. Also, the decision maker(DM) can modulate some robust parameters according to the actual robust demands which makes this approach have more practical significance.

关键词： Robustness optimization Evolutionary computation Delta modulation Simulation Algorithm design and analysis Search problems

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Automatic Sleep Stage Classification by a Density-Distance-Based K-means Clustering Algorithm with Amendments

Automatic Sleep Stage Classification by a Density-Distance-B...

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International Congress on Image and Signal processing, BioMedical Engineering and Informatics

作者： Ying Yu Bei Wang Jing Jin Xingyu Wang Key Laboratory of Advanced Control and Optimization for Chemical Processes East China University of Science and Technology Ministry of Education Shanghai China

Sleep staging has important significance for the monitoring, prevention and treatment of sleep disorders. In this study, the clustering algorithm of K-means is investigated in order to realize the automatic sleep stage classification. Unlike the traditional K-means clustering algorithm, density-distance-based processing procedures were presented to determine the cluster centers. Additionally, the clustering results were amended based on the regularity of sleep stages during one's overnight sleep process. The presented algorithm was tested and analyzed in detail with six subjects from the Sleep-EDF database. The averaged accuracy is about 74%. Comparing with the traditional K-means clustering algorithm, the developed algorithm achieved more accurate and reasonable sleep staging results which can be an assistant tool for clinical inspection.

关键词： Sleep Clustering algorithms Electroencephalography Support vector machines Signal processing algorithms Electrodes Databases

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Dynamic optimization with an improved θ-PSO based on memory recall

Dynamic optimization with an improved θ-PSO based on memory...

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World Congress on Intelligent control and Automation (WCICA)

作者： Weimin Zhong Jianliang Xing Yi Liang Feng Qian Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai China

A comparative study of θ-PSO and its improved model with partial particles randomization strategy on their abilities of tracking extrema in dynamic environments was carried out in our earlier work. And the results shown that θ-PSO has better performance in dynamic optimization than standard PSO. In this paper, an improved θ-PSO with memory recall and varying scale randomization strategy (θ-PSO-MR) is put forward. The eligible memory particles are recalled when the landscape changes. And the vary scale randomization is introduced through the evolution to maintain the swarm diversity. The offline error in the non-trivial multimodal dynamic functions MPB indicates that this improved θ-PSO deals well with the complex dynamic tracking and optimization. And in some cases, θ-PSO-MR outperforms θ-PSO-Rn for the introduction of memory recall.

关键词： optimization Particle swarm optimization Heuristic algorithms IEEE services Laboratories

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IPSO: An Immune based PSO Supervised Learning System for Incremental Learning

IPSO: An Immune based PSO Supervised Learning System for Inc...

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The 8th World Congress on Intelligent control and Automation(第八届智能控制与自动化世界大会 WCICA 2010)

作者： Xuan Zhou Jin Yu Rongbin Qi Feng Qian Zhenlei Wang Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai China

PSO has been proved as an effective supervised learning system in recent years, but it's not an effective method for incremental learning problems. Aiming at the incremental learning target for classification, a hybrid algorithm of Particle Swarm optimization (PSO) and Artificial Immune System (AIS) called Immune based PSO (IPSO) is presented in this paper. IPSO inherits the incremental learning ability of AIS. In IPSO, training data is presented to the algorithm one by one, and the training proceed is a one-shot incremental algorithm. Besides, the swarm does not converge to a single solution;instead, each particle is a part of the classifier, and the whole memory population is taken as the integral classifier to the problem. Compared the results of standard PSO and IPSO in several benchmark problems from the UCI data sets, we found that IPSO achieved a better classification accuracy than standard PSO in most cases. It is also competitive with some of the algorithms most commonly used for classification.

关键词： Classification algorithms Particle swarm optimization Accuracy Training Machine learning Diabetes Iris

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Distributed reliable L2 — L∞ state estimation for discrete-time delayed neural networks with missing measurements

Distributed reliable L2 — L∞ state estimation for discrete...

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World Congress on Intelligent control and Automation (WCICA)

作者： Hao Zhang Huaicheng Yan Mengling Wang Hongbo Shi Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education East China University of Science and Technology Shanghai China

ISBN: (纸本)9781467384155

This paper is concerned with the distributed L 2 - L ∞ state estimation for discrete-time delayed neural networks with missing measurements and sensor failures. The model of neural networks is constructed by the combination with nonlinear functions and state space expressions. Time-varying delays are assumed to have the lower bounds and the upper bounds, and the missing measurements and the sensor failures are described by a stochastic Bernoulli distributed variable and uncertainties, respectively. By using a distributed network, Kronecker product, linear matrix inequalities (LMIs) technology and the solving optimization problems, a sufficient condition is derived to ensure that the filtering error system is stochastically asymptotically stable and achieves the optimal L 2 - L ∞ performance. Finally, a simulation example is given to demonstrate the effectiveness of the proposed method.

关键词： Robot sensing systems Neural networks Delays State estimation Linear matrix inequalities Reliability Sparse matrices

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