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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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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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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 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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Real-time control signal extraction based on instantaneous power of surface electromyogram

Real-time control signal extraction based on instantaneous p...

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International Conference on BioMedical Engineering and Informatics (BMEI)

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

The previous studies on Human-machine Interface (HMI) based on surface electromyogram (SEMG) control seldom considered individual difference. In this paper, we proposed a novel method to distinguish the handedness of subjects, and extract the real-time control signals from SEMG. SEMG signals recorded at left calf, right calf, left shoulder and right shoulder were analyzed. The instantaneous power of SEMG signals were calculated based on Filter technique and short-time Fourier transformation (STFT).The handedness was discriminated to determine the proper threshold for different subjects. The real-time control signals were generated automatically by comparing the instantaneous power and the thresholds preset.

关键词： Muscles Electrocardiography Band pass filters Time frequency analysis Real time systems Robots Wheelchairs

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A novel approach for moving object detection based on improved particle swarm optimization algorithm

A novel approach for moving object detection based on improv...

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International Conference on control and Automation (ICCA)

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

In order to extract from the video sequence in a complete and consistent moving target, a novel algorithm for video object segmentation based on improved particle swarm optimization (IPSO) is presented. The algorithm fuses brightness segmentation and color information at `region level', as to make up for conventional `pixel level' approaches. The IPSO is taken into account for spatial segmentation of the video frame, which combines the mixture Gaussian model of temporal framework in achieving better segmentation. Adapting to the real-time video surveillance, the proposed algorithm can speed up the process of image segmentation, and make background modeling accurately to update. Comparisons were performed with other method that the proposed algorithm can detect intact moving objects even when objects appear and disappear suddenly. The experiment across different types of video shows the efficiency and stability of video object segmentation by the novel approach.

关键词： Object detection Particle swarm optimization Image segmentation Object segmentation Data mining Video sequences Fuses Brightness Video surveillance Stability

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Integration of Multi-feature for Moving Target Detection Algorithm

Integration of Multi-feature for Moving Target Detection Alg...

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International Conference on Electrical and control Engineering (ICECE)

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

Moving object segmentation and marking object area is one of the key technologies of intelligent surveillance. In this paper, combining the background modeling in pixel level, the proposed algorithm presented gradient-based spatial characteristics in order to improve detection accuracy. For the trade-off problem of background modeling, established the velocity characteristics for each property of moving targets, to make online updates of the GMM more adaptable. The results indicate that this technique can improve the efficiency and stability of detection.

关键词： Hidden Markov models Pixel Adaptation model Image segmentation Computational modeling Object detection Computer vision

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Estimation of Distribution Algorithm sampling under Gaussian and Cauchy distribution in continuous domain

Estimation of Distribution Algorithm sampling under Gaussian...

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International Conference on control and Automation (ICCA)

作者： Na Luo Feng Qian Automatic Institute Key Laboratory of Advanced Control and Optimization of Chemical Processes Ministry of Education State-Key Laboratory of Chemical Engineering East China University of Science and Technology Shanghai China

Estimation of Distribution Algorithm is a new population based evolutionary optimization method and it generates new population from probability distribution model. Like most evolutionary algorithms, it is easy to trap into local optimums. In order to avoid this shortcoming, Gaussian and Cauchy probability density function are mixed as probability distribution model. For continuous problems, a new estimation of distribution algorithm sampling under the mixed model is presented. New individuals are generated not only from Gaussian distribution but sometimes from Cauchy distribution in order to keep diversity. The selection strategy of Gaussian and Cauchy distribution are also discussed. The new algorithm is tested on five benchmark functions and results are compared with basic and estimation of distribution algorithm with Cauchy mutation.

关键词： Sampling methods Genetic mutations Electronic design automation and methodology Evolutionary computation Laboratories Probability distribution Gaussian distribution Automatic control chemical processes control engineering education

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Particle swarm optimization algorithm of self-adaptive parameter based on ant system and its application

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Kongzhi Lilun Yu Yingyong/control Theory and Applications 2010年第11期27卷 1479-1488页

作者： Yang, Fan Hu, Chun-Ping Yan, Xue-Feng Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education East China University of Science and Technology Shanghai 200237 China Shanghai Nuclear Engineering Research and Design Institute Shanghai 200233 China

To adjust the inertia weight in particle swarm optimization(PSO), we propose a novel self-adaptive particle swarm optimization algorithm based on ant system(AS-PSO). First, the inertia weight space is divided into several regions;each of them is given the same initial intensity of pheromone trails. The probability for selecting a parameter region for each particle is determined by the intensity of the region pheromone trails and the particle's a priori knowledge of the search space. The evolution search is then performed in spaces of solutions. Finally the trail of the regions is updated according to the information of evolution. Experiments indicate that the promising AS-PSO algorithm realizes the evolution and the self-adaptation of the inertia weight by ant colony algorithm without increasing the function calls in evaluation. Results show that AS-PSO obviously outperforms the original self-adaptive PSO and the APSO-VI, in which the parameter is adjusted according to the velocity information. Furthermore, satisfactory results have been obtained when AS-SPO algorithm is applied to estimate the parameter of complex system models.

关键词： Particle swarm optimization (PSO)

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Early Warning Technology for Cracking Severity Basedon Improved Cultural Differential Algorithm

Early Warning Technology for Cracking Severity Basedon Impro...

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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： Yunxia Pu Mandan Liu Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai 200237 China

Effective monitoring and early warning for cracking severity are directly related to the ethylene production stability and the overall economic benefits. Elman neural network is used to establish the early warning model of cracking severity, and an improved Cultural Differential Evolution algorithm(ICDE) is proposed to training the model. This hybrid algorithm integrates Cultural algorithm(CA) and Differential Evolution algorithm(DE) together, and increases some measure indicators of evolution direction, to make the algorithm more adaptive. Simulation results show that this method gains accurate warning signals to the cracking working conditions timely and effectively, obtained satisfactory results.

关键词： Cultural differences Artificial neural networks Furnaces Training Next generation networking Predictive models Production

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