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World Congress on Intelligent control and Automation (WCICA)

作者： Xuehai Wang Feng Ding Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education) Jiangnan University Wuxi PR China

This paper presents two recursive identification algorithms for bilinear-parameter models: a decomposition based stochastic gradient algorithm and a decomposition based recursive least squares algorithm. The key is to decompose a bilinear-parameter model into two fictitious subsystems, and to identify the parameters of each subsystem by replacing the unknown variables in the information vectors with their estimates. The simulation results show the performances the proposed algorithms.

关键词： Signal processing algorithms Mathematical model Nonlinear systems Stochastic processes Vectors Parameter estimation Least squares approximations

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RNGA Loop Pairing Criterion for Multivariable Systems Subject to a Class of Reference Inputs

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IFAC Proceedings Volumes 2014年第3期47卷 4721-4726页

作者： Qiang Chen Xiaoli Luan Fei Liu Key Laboratory of Advanced Process Control for Light Industry Ministry of Education and the Institute of Automation Jiangnan University Wuxi 214122 P.R.China

This paper aims to generalize the loop interaction measurement, relative normalized gain array (RNGA), for multivariable systems regarding a class of reference inputs. In the existing studies, RNGA loop pairing criterion is analyzed and widely utilized on the basis of detailed assumption of step reference input. For multivariable systems under step, ramp and other general types of set-point changes, the general loop pairing technique is put forward, and the average residence time is calculated in terms of first order plus delay time and second order plus delay time processes. The analysis results show RNGA based control-loop configuration is independent of input signals, and available to multivariable systems for various reference inputs. Several examples are employed to demonstrate the effectiveness and universality of the pairing approach of this paper.

关键词： Multivariable systems Loop pairing Relative normalized gain array Interaction measurement

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Identification of Hammerstein MIMO systems using the key-term separation principle

Identification of Hammerstein MIMO systems using the key-ter...

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World Congress on Intelligent control and Automation (WCICA)

作者： Qianyan Shen Feng Ding Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education) Jiangnan University Wuxi PR China

This paper focuses on parameters estimation problems of multivariable nonlinear systems. A hierarchical least squares algorithm is proposed by using key-term separation principle and hierarchical identification principle. The algorithm has lower computational load than the existing over-parametrization methods. Finally, a numerical example is given to show the effectiveness of the proposed algorithm.

关键词： MIMO Vectors Heuristic algorithms Mathematical model Noise Computational modeling Parameter estimation

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Maximum likelihood based multi-innovation stochastic gradient estimation for controlled autoregressive ARMA systems using the data filtering technique

Maximum likelihood based multi-innovation stochastic gradien...

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World Congress on Intelligent control and Automation (WCICA)

作者： Feiyan Chen Feng Ding Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education) Jiangnan University Wuxi PR China

This paper considers parameter estimation problems of a controlled autoregressive ARMA system. We decompose this system into two subsystems, use the data filtering technique to derive a maximum likelihood multi-innovation stochastic gradient algorithm. The simulation results show that the proposed algorithm has a higher computational efficiency than the maximum likelihood gradient algorithm and the filtering-based maximum likelihood stochastic gradient algorithm.

关键词： Maximum likelihood estimation Stochastic processes Parameter estimation Vectors Mathematical model Signal processing algorithms Computational modeling

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A kinetic model structure for simultaneous saccharification and fermentation of glutinous rice

A kinetic model structure for simultaneous saccharification ...

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American Society of Agricultural and Biological Engineers Annual International Meeting 2014, ASABE 2014

作者： Liu, Dengfeng Zhang, Hongtao Xu, Baoguo Tan, Jinglu Key Laboratory of Industrial Advanced Process Control for Light Industry of Ministry of Education Jiangnan University Wuxi214122 China Department of Bioengineering University of Missouri ColumbiaMO65211 United States School of Bioengineering Jiangnan University Wuxi214122 China

ISBN: (纸本)9781632668455

Simultaneous saccharification and fermentation (SSF) of glutinous rice was performed by using o-amylase, glucoamylase, and rice wine yeast strain Saccharomyces cerevisiae Su-25. Experiments were carried out at two different locations, and the main products were identified and measured by HPLC. A low-order kinetic model structure (forms or constructs of model with adjustable parameters) was proposed based on the major chemical reactions in the SSF process. The model structure was then tested for its abilities to capture the main kinetic variations after parameter optimization by a least-squares algorithm. The proposed model structure was found useful in representing measured kinetic variations. The estimated reactions rates correctly reflected the variations observed from the experiments and provided insights into the reaction processes. While additional research is warranted for further validation and refinement, the proposed model structure shows promise for describing the simultaneous saccharification and fermentation process of glutinous rice.

关键词： Yeast

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IR remote sensing image registration based on multi-scale feature extraction

IR remote sensing image registration based on multi-scale fe...

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2014 International Joint Conference on Neural Networks, IJCNN 2014

作者： Kong, Jun Jiang, Min Sun, Yi-Ning Key Laboratory of Advanced Process Control for Light Industry Ministry of Education Jiangnan University Wuxi214122 China College of Electrical Engineering Xinjiang University Urumqi830047 China Institute of Intelligent Machines Chinese Academy of Sciences Hefei230031 China

ISBN: (纸本)9781479914845

Infrared remote sensing image has poor contrast and lower SNR so that real-time and robustness are not superior in image registration. In order to solve it, a novel registration based on Multi-scale feature extraction is proposed in this paper. This algorithm is designed in two aspects. Firstly, Gaussian convolution template size adjusts adaptively with the increasing of scale factors. Then the Multi-space is reconstructed. Secondly, feature points bidirectional matching based on the City-block distance is introduced into image registration. So the real-time performance and robustness are enhanced further. Finally, the experimental results showed that by this improved algorithm the infrared remote sensing images are registered more quickly and accurately than by traditional SIFT algorithm. © 2014 IEEE.

关键词： Image registration

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Unbiased Minimum-Variance Filtering for Systems with Randomly Multi-Step Sensor Delays

Unbiased Minimum-Variance Filtering for Systems with Randoml...

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Annual Conference of the IEEE Industrial Electronics Society

作者： Yilian Zhang Fuwen Yang Qing-Long Han Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education East China University of Science and Technology Centre for Intelligent and Networked Systems Central Queensland University

ISBN: (纸本)9781479940318

In this paper, a linear unbiased minimum-variance filtering problem is considered for a class of systems with randomly multi-step sensor delays. A new mathematical model is established for the multi-step sensor delays. Different from the augmented method for dealing with delayed systems, a linear unbiased minimum-variance filter design method is proposed without augmenting the state vector, which effectively reduces the filter dimensions. A recursive algorithm for calculating the filter gain matrix is developed. The simulation results illustrate the effectiveness of the proposed method.

关键词： Networked control systems Filtering Recursive estimation Kalman filters Delay systems Stochastic processes Sensor system Communication networks

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Estimation of Missing Data in Prediction of the Maximum Wind Speed of Typhoon with Probabilistic PCA

Estimation of Missing Data in Prediction of the Maximum Wind...

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2014年国际计算机科学与软件工程学术会议

作者： Hong-Li LI Xin WANG Key Laboratory of Advanced Process Control for Light Industry(Ministry of Education) Jiangnan UniversityWuxi 214122PR China

In the prediction model for the maximum wind speed of typhoon,the number of the input variables is very large,so the situation of missing data is easy to ***,regression analysis can't deal with this *** paper proposes a method to predict missing data based on probabilistic principal component analysis (PPCA),which treats the abnormal data and the predictive variables as missing variables,and it takes Mahalanobis distance to reflect the exact relationship among process *** experimental result shows that this method is more flexible than the regression analysis,and it is more accurate.

关键词： Probabilistic principal component analysis Missing data reconstruction Mahalanobis distance Software sensor

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An advanced particle swarm optimization with Gaussian distribution operators

An advanced particle swarm optimization with Gaussian distri...

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2013 International Conference on Future Computer and Information Technology(ICFCIT 2013)

作者： Maolong Xi Ye Zou Guanghong Zhou Jun Sun School of Control Technology Wuxi Institute of Technology Key Laboratory of Advanced Process Control for Light Industry(Ministry of Education) Jiangnan University

Parameter selection is an essential work which influences the performance of a particle swarm optimization algorithm(PSO). In evolutionary equations of a PSO algorithm, two uniform random numbers are employed to perform the global exploration search and local exploitation, and then the particles can only fly in a limited search space. In this paper, a novel strategy is proposed by introducing Gaussian distribution operators into PSO and new evolutionary equations are given, which can expand the activity range of particles and increase the probability of finding global solutions of problems. Simulation results show the proposed method is effective and efficient compared with other variants of PSO.

关键词： PSO,uniform distribution,Gaussian distribution

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Distributed Estimation over Analog Fading Channels Using Constant-Gain Estimators

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IFAC Proceedings Volumes 2014年第3期47卷 2872-2877页

作者： Nan Xiao Lihua Xie Yugang Niu Yiguang Hong Key Laboratory of Advanced Control and Optimization for Chemical Process (East China University of Science and Technology) Ministry of Education Shanghai 200237 China EXQUISITUS Centre for E-City School of Electrical and Electronic Engineering Nanyang Technological University Singapore 639798 Singapore Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing 100190 China

This paper considers the distributed estimation of an unstable target via constant-gain estimators under local communications and channel fading. The communication graph is assumed to be fixed and undirected, and the channel fading is assumed to be identical. Necessary and sufficient conditions on communication network over which the state of the unstable target can be estimated in the mean square sense are given for both continuous-time and discrete-time cases, which reveal the fundamental limitation on distributed estimation induced by local communications, channel fading, and target dynamics. In addition, our results for the case without channel fading and the case with separate communications are consistent with the results in the literature.

关键词： Analog fading channel constant-gain estimator distributed estimation mean square detectability

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