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作者： Liu, Zhitao Su, Hongye State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China

ISBN: (纸本)9781424474264

In this paper dynamic power shaping control is applied to a continuous fermentation process which is a benchmark problem in the nonlinear process systems. In contrast with other techniques, dynamic power shaping control yields a nonlinear state feedback that ensures the asymptotic stability of the desired equilibrium of the fermentation process. Moreover, as the biomass concentration is hardly measurable, a observer-based power shaping controller is designed and can also make the fermentation asymptotically stable. Simulation results are shown to demonstrate the close-loop performance of the proposed method. © 2010 AACC.

关键词： Asymptotic stability

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A Energy Balanced Deployment for Linear Wireless Sensor Networks

A Energy Balanced Deployment for Linear Wireless Sensor Netw...

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2011 International Conference on Computer Science and Network technology(2011计算机科学与网络技术国际会议 ICCSNT 2011)

作者： Zhengjie Wang Xiaoguang Zhao Xu Qian College of Information and Electrical Engineering Shandong University of Science and Technology Qi State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese School of Mechanical Electronic & Info. Engineering China University of Mining & Technology (Beijin

ISBN: (纸本)9781457715860

Linear wireless sensor networks are characterized by a linear topology and multihop forwarding patterns. Sensors closer to the base station are usually required to forward a large amount of traffic for sensors farther from the base station, leading to the environment cannot be effectively monitored. The energy consumption balance is achieved in the paper by adjusting the distance between nodes. The distance of nodes near the base station become short because they have to forward the more traffic. The node deployment is converted to optimization problems of distance distribution between nodes. The network lifetime under different number of forwarding region is discussed. Simulation results show that this deployment strategy obtains the energy consumption balance.

关键词： wireless sensor networks optimal communication distance energy consumption balance distance optimal

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Mining fuzzy association rules in data streams

Mining fuzzy association rules in data streams

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2010 2nd International Conference on Computer Engineering and technology, ICCET 2010

作者： Chen, Peng Su, Hongye Guo, Lichao Qu, Yu State Key Lab. of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China

ISBN: (纸本)9781424463503

Many algorithms have been proposed for mining fuzzy association rules in static datasets with quantitative attributes. However, there is few study on mining fuzzy association rules in data streams. This paper presents an algorithm FFI-Stream for fuzzy association rules mining in data streams. Efficient techniques are presented to find fuzzy association rules in data streams using time based sliding window model. The clustering technique is used to find the fuzzy sets. Membership Function Bias measure (MFB-measure) is introduced to evaluate the membership function in each sliding window. Selectively Updating Mechanism (SUM) and Projected Summaries (PS) are proposed to update the fuzzy sets dynamically. Then, experiments are carried out on both synthetic and real life datasets. The results show that the algorithm is effective and efficient. © 2010 IEEE.

关键词： Membership functions

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Agent-based traffic simulation and traffic signal timing optimization with GPU

Agent-based traffic simulation and traffic signal timing opt...

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International Conference on Intelligent Transportation

作者： Zhen Shen Kai Wang Fenghua Zhu State Key Laboratory of Intelligent Control and Management of Complex Systems Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences (CAS) Beijing China Center of Military Computational Experiments and Parallel Systems Technology and College of Mechatronics Engineering and Automation National University of Defense Technology Changsha Hunan China

With the advantage of simulating the details of a transportation system, the “microsimulation” of a traffic system has long been a hot topic in the Intelligent Transportation systems (ITS) research. The Cellular Automata (CA) and the Multi-Agent System (MAS) modeling are two typical methods for the traffic microsimulation. However, the computing burden for the microsimulation and the optimization based on it is usually very heavy. In recent years the Graphics Processing Units (GPUs) have been applied successfully in many areas for parallel computing. Compared with the traditional CPU cluster, GPU has an obvious advantage of low cost of hardware and electricity consumption. In this paper we build an MAS model for a road network of four signalized intersections and we use a Genetic Algorithm (GA) to optimize the traffic signal timing with the objective of maximizing the number of the vehicles leaving the network in a given period of time. Both the simulation and the optimization are accelerated by GPU and a speedup by a factor of 195 is obtained. In the future we will extend the work to large scale road networks.

关键词： Graphics processing unit Vehicles Roads Optimization Computational modeling Genetic algorithms Timing

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Recursive closed-loop subspace identification based on predictor Markov parameters

Recursive closed-loop subspace identification based on predi...

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2010 3rd International Congress on Image and Signal Processing, CISP 2010

作者： Li, Youfeng Xie, Lei Su, Hongye Chu, Jian State Key Lab. of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou 310027 China

ISBN: (纸本)9781424465149

Recursive subspace identification methods have been an active area of research in recent years. In this paper, a recursive version of a closed-loop subspace method is proposed for on-line system modeling. The algorithm is based on the predictor Markov parameters and is prone to a very simple and computationally attractive implementation. A numerical example shows the effectiveness of the proposed algorithm on the problem of closed-loop recursive identification. ©2010 IEEE.

关键词： Markov processes

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Tracking control of a class of nonlinear systems with unknown periodic time-varying parameters via output feedback

Tracking control of a class of nonlinear systems with unknow...

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作者： Chen, Pengnian Fang, Xueyi Qin, Huashu College of Mechatronics Engineering China Jiliang University Hangzhou 310018 China State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou 310027 China Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing 100080 China

ISBN: (纸本)9789881725592

The paper considers the problem of tracking control of a class of nonlinear systems with unknown periodic time-varying parameters via dynamic output feedback. Both matched disturbances and unmatched disturbances are considered. A new method for asymptotic rejection of multiple general periodic disturbances is presented. The method can guarantee that all signals in the closed loop system are bounded and the tracking error approaches zero. © 2011 Chinese Assoc of Automati.

关键词： Disturbance rejection

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Multiple-prior-knowledge neural network for industrial processes

Multiple-prior-knowledge neural network for industrial proce...

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IEEE International Conference on Automation and Logistics

作者： Lou, Haichuan Su, Hongye Xie, Lei Gu, Yong Rong, Gang National Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou Zhejiang Province China

ISBN: (纸本)9781424483754

A novel multiple-prior-knowledge neural network for industrial processes is proposed. Diversity of priori knowledge from industrial processes are discovered and embedded into three-layer hybrid feedforward neural network in the form of penalty function or nonlinear constraints. Meanwhile, Sequential quadratic programming method is selected as the network learning algorithm. Compared with BP neural network and an industrial neural network (the Bounded Derivative neural network, BDNN), MPKN network not only satisfies a certain process mechanism and keeps the process safety, but also has better generalization. Its effective performance is validated with the modelling of a nonlinear process of Continuous-stirred tank reactor. © 2010 IEEE.

关键词： Accident prevention

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Adaptive least contribution elimination kernel learning approach for rubber mixing soft-sensing modeling

Adaptive least contribution elimination kernel learning appr...

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2010 IEEE International Conference on Intelligent Computing and Intelligent systems, ICIS 2010

作者： Gao, Yan-Chen Ji, Jun Wang, Hai-Qing Li, Ping State Key Laboratory of Industrial Control Technology Institute of Industrial Process Control Zhejiang University Hangzhou China

ISBN: (纸本)9781424465835

Rubber mixing process is a typical non-linear fed-batch process without well developed mechanism. Soft-sensing modeling of the mixture's Mooney viscosity is crucial and challenging since this index is an important process criterion to judge the quality of rubber compounds while the measurement of Mooney viscosity is time-consuming and laborious to assay. Furthermore, the mixing process is drifting and volatile even noisy;only few data samples could be used to modeling. In present paper, an adaptive least contribution elimination kernel learning (ALCEKL) approach is proposed to predict the Mooney viscosity. It adopts a sparsity strategy of least contribution elimination and presents a buffer based learning algorithm associated with improved space angle index (SAl) strategy. Experiments on field data indicate that proposed approach is more robust and accurate than other kernelized modeling methods with feasible computational complexity under field circumstances. ©2010 IEEE.

关键词： Rubber

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A biologically inspired model for pattern recognition

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Journal of Zhejiang University-Science B(Biomedicine & Biotechnology) 2010年第2期11卷 115-126页

作者： Eduardo GONZALEZ Hans LILJENSTROM Yusely RUIZ Guang LI Department of Biomedical Engineering Zhejiang University Hangzhou 310027 China Department of Energy and Technology Swedish University of Agricultural Sciences Box 7013 SE-750 Sweden National Laboratory of industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou 310027 China

In this paper, a novel bionic model and its performance in pattern recognition are presented and discussed. The model is constructed from a bulb model and a three-layered cortical model, mimicking the main features of the olfactory system. The olfactory bulb and cortex models are connected by feedforward and feedback fibers with distributed delays. The Breast Cancer Wisconsin dataset consisting of data from 683 patients divided into benign and malignant classes is used to demonstrate the capacity of the model to learn and recognize patterns, even when these are deformed versions of the originally learned patterns. The performance of the novel model was compared with three artificial neural networks (ANNs), a back-propagation network, a support vector machine classifier, and a radial basis function classifier. All the ANNs and the olfactory bionic model were tested in a benchmark study of a standard dataset. Experimental results show that the bionic olfactory system model can learn and classify patterns based on a small training set and a few learning trials to reflect biological intelligence to some extent.

关键词： Olfactory system Neural network Bionic model Pattern recognition

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Statistical Monitoring of industrial Process Faults Using Local Independent Component Regression

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IFAC Proceedings Volumes 2011年第1期44卷 2833-2838页

作者： Jiusun Zeng Lei Xie Uwe Kruger Chuanhou Gao Zhe Li Jianming Zhang State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou 310027 P.R. China Department of Electrical Engineering The Petroleum Institute P.O.Box 2533 Abu Dhabi U. A. E. (Tel:971-2-607-5150) Department of mathematics Zhejiang University Hangzhou 310027 P.R. China

Abstract This article utilizes the independent component regression (ICR) algorithm which is capable of extracting non-Gaussian components from both input and output variables for monitoring of complex industrial systems. The components extracted by ICR are relevant in the sense of of mutual information rather than second order statistics. Statistical local approach is then incorporated into the ICR algorithm to monitor changes in process and model parameters. The proposed monitoring strategy is capable of detecting non-Gaussian parameter changes and the monitoring task is simplified by establishing low dimensional Gaussian monitoring statistics. An application study to fluidized bed reactor shows that the proposed monitoring strategy are efficient in detecting process faults.

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