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检索条件"机构=Key Laboratory of Aflvanced Control and Optimization for Chemical Processes"

共 485 条记录，以下是291-300 订阅

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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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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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Impulsive consensus of leader-following nonlinear multi-agent systems under DoS attacks

Impulsive consensus of leader-following nonlinear multi-agen...

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Annual Conference of the IEEE Industrial Electronics Society

作者： Yiting Wang Wangli He Ministry of Education The Key Laboratory of Advanced Control and Optimization for Chemical Processes East China University of Science and Technology Shanghai China

This paper investigates impulsive consensus of nonlinear multi-agent systems in the leader-following framework subject to denial-of-service (DoS) attacks. The case that the communication network suffers from DoS attacks is considered, which will destroy the communication link, resulting in switching topologies. Based on the assumption that the system can recover from DoS attacks, sufficient conditions on the design of the impulse interval and the impulse attack ratio that the system can sustain is provided. A numerical example is given to validate the effectiveness of our theoretical results.

关键词： Multi-agent systems Topology Switches Denial-of-service attack Network topology Communication networks

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Pulse-Modulated Intermittent control for Scaled Consensus of Second-Order Multi-Agent Systems

Pulse-Modulated Intermittent Control for Scaled Consensus of...

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IEEE International Symposium on Industrial Electronics (ISIE)

作者： Hualiang Guo Wangli He The Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai China

The scaled consensus problem for second-order multi-agent systems is studied in this paper. A novel scaled consensus algorithm combining impulsive control and sampled control is proposed to make the agents' states achieve a common quantity but of their own scales. In the proposed algorithm, a special function, named intermittent function, is introduced to represent the control duration and the rest duration. Specifically, by selecting special functions as the proposed intermittent functions, the scaled consensus algorithm can drop into impulsive control or sampled control. Several necessary and sufficient conditions are derived to ensure scaled consensus of the controlled second-order multi-agent systems under directed graph. An example is provided to validate the theoretical analysis.

关键词： Multi-agent systems Convergence Graph theory control systems Satellites Continuous time systems Nickel

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Improving the performance of online classifier by removing the error samples from offline training data

Improving the performance of online classifier by removing t...

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International Conference on Computer and Communications (ICCC)

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

Brain-computer interface (BCI) plays an important role in helping the people with severe motor disability. In event-related potential (ERP) based BCIs, subjects were asked to count the target stimulus in the offline experiment, the recorded electroencephalogram (EEG) data was used to train the classification mode. However, subjects may make mistakes in counting the target stimulus or be affected by the non-target stimulus. The target trials may not contain expected ERPs and the non-target trials may contain unexpected ERPs, which was called error samples. This paper intends to survey whether the classification accuracy could be improved after removing these error samples from offline training data. The result showed that the online performance of BCI system could be improved after selecting the offline samples for training the classification mode.

关键词： Electroencephalography Feature extraction Training Classification algorithms Electrodes Band-pass filters Brain modeling

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An Optimized BCI System Based on P300 and Visual Mismatch Negativity

An Optimized BCI System Based on P300 and Visual Mismatch Ne...

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International Conference on Information Science and control Engineering (ICISCE)

作者： Sijie Zhou Jing Jin Yu Zhang Xingyu Wang 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)9781467368513

This paper presents a brain-computer interface (BCI) in which the face paradigm was optimized for the visual mismatch negativity (MMN). There were 12 cells in a LCD monitor. A single letter was at the bottom of each cell. In the new paradigm, a color face appeared above each of the 12 cells randomly while the gray faces appeared in others 11 cells. A traditional face paradigm with single character pattern was compared. Three healthy subjects participated in the experiment. Results showed that the new paradigm elicited larger N200 and N400 components than traditional face paradigm and had better performance in online session. The results demonstrated the advantages of the new paradigm in our P300 speller system.

关键词： Face Frequency modulation Visualization Accuracy Brain-computer interfaces Electroencephalography Electrodes

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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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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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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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