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3rd International Workshop on advanced Computational Intelligence and Intelligent Informatics, IWACIII 2013

作者： Wang, Shan-Shan Du, Wen-Li Chen, Xu Qian, Feng East China University of Science and Technology Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education Shanghai200237 China

Dynamic multi-objective optimization is a complex and difficult research topic of process systems engineering. In this paper, a modified Bare-bones MOPSO algorithm is proposed that takes advantage of few parameters of bare-bones algorithm. To avoid premature convergence, Gaussian mutation is introduced;and an adaptive sampling distribution strategy is also used to improve the exploratory capability. Moreover, a circular crowded sorting approach is adopted to improve the uniformity of the population distribution. Finally, by combining the algorithm with control vector parameterization, an approach is proposed to solve the dynamic optimization problems of chemical processes. It is proved that the new algorithm performs better compared with other classic multi-objective optimization algorithms through the results of solving three dynamic optimization problems.

关键词： Bone

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Quantized H∞ control for networked systems with randomly multi-step transmission delays

Quantized H∞ control for networked systems with randomly mu...

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作者： Zhang, Yilian Yang, Fuwen Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education East China University of Science and Technology Shanghai200237 China

ISBN: (纸本)9781479940325

In this paper, a quantized H∞ control problem for networked control systems (NCSs) subject to randomly multi-step transmission delays is investigated. A quantizer is used before the measurement signal enters the communication network and the randomly multi-step transmission delays which are described by a mathematical model are considered during the transmission through the network. Sufficient conditions are derived for the considered system to satisfy the H∞ norm constraint subject to the randomly multi-step transmission delays. Simulation results demonstrate the effectiveness of the proposed method. © 2014 IEEE.

关键词： Networked control systems

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Locust behaved particle swarm optimization technique 3

Locust behaved particle swarm optimization technique

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3rd International Workshop on advanced Computational Intelligence and Intelligent Informatics, IWACIII 2013

作者： Zhong, Wei-Min Xie, Xue-Qin Liang, Yi Luo, Na Zhang, Juan Qian, Feng East China University of Science and Technology Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education Shanghai200237 China

The collective behavior of certain animals and insects has the characteristic of self-organization. The simple interactions among individuals can produce complex adaptive patterns at the level of the group. Recently, new scientific investigation pointed out that desert locusts show extreme phenotypic plasticity in transforming between the lonely phase and the swarming gregarious phase depending on the population density, which is controlled by a serotonin called 5-hydroxytryptamine. In this paper, based on the mechanism of the locusts' collective behavior, a new particle swarm optimization technique called LBPSO is studied. The number of swarms is self-adaptively adjusted by the acquired outstanding particles coming from behind the previous global best solution. The swarm sizes are related to the corresponding serotonin 5-hydroxytryptamine which is determined by the optimization parameters such as global best, iteration number etc. And each swarm adopts one of three rules below according to its density, generalized social evolution strategy, generalized cognition evolution strategy and the independent moving strategy. A comparative study of LBPSO, SPSO, Improved SPSO and the original PSO on their ability of tracking optima is carried out. And the results under four static benchmark functions and a dynamic function generator MPB show that LBPSO outperforms the other three functions in both static and dynamic landscapes due to the introduced locusts' collective behavior.

关键词： Iterative methods

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An improved particle swarm optimization algorithm based on two kinds of chaotic maps 3

An improved particle swarm optimization algorithm based on t...

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3rd International Workshop on advanced Computational Intelligence and Intelligent Informatics, IWACIII 2013

作者： Li, MingHao Wang, XueWu Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai200237 China

Particle swarm optimization algorithm tends to fall into local optimum sometimes. To resolve this problem, an improved particle swarm optimization algorithm based on two kinds of different chaotic maps is proposed. The algorithm produces primitive chaotic particle swarm using the uniform distribution of Tent map and improves the diversity of search. When the particle swarm evolves to a local optimum, the chaotic mutation operator produced by Logistic map is adopted to form a disturbance on the swarm to drive particle swarm jump out of local optimum and approach the global optimum. Meanwhile, an adaptive inertia weight factor is introduced to adjust particles inertia weight factor adaptively, which forms a new 2-chaotic maps embedded adaptive particle swarm optimization algorithm (2-CMEAPSO) that can fully utilize the randomness and ergodicity of the chaotic motion to enhance optimization capability. Experimental results show that the improved algorithm can efficiently overcome the premature of standard particle swarm optimization algorithm. Besides, it has stronger global optimization ability and higher accuracy than the basic particle swarm optimization algorithm.

关键词： Global optimization

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Fault diagnosis of CTA hydrogenation process based on KPCA 3

Fault diagnosis of CTA hydrogenation process based on KPCA

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3rd International Workshop on advanced Computational Intelligence and Intelligent Informatics, IWACIII 2013

作者： Li, Bo Li, Zhi Luo, Na Zhong, Weimin East China University of Science and Technology Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education Shanghai200237 China

As the large amounts of operate data collected from Distributed control System (DCS) often contain outliers and these data are more complexity and nonlinearity. They can't be used directly to model, optimization and fault diagnosis. In fault diagnosis, the existence of outliers can destroy the covariance structure of Kernel Principal Component Analysis (KPCA), which cause the model can't really reflect the actual normal condition. In this paper, KPCA method is adopted to establish the normal statistic monitor model from the historical data which can represent the normal industrial operate condition. First, the outlier detection algorithm is used to eliminate outliers among normal work condition. Then the primary statistic model for fault diagnosis of the Squared Prediction Error (SPE) and T2 are established according to the data exclude outliers. The effectiveness of this fault diagnosis is demonstrated by the operate data of industrial Crude Terephthalic Acid (CTA) hydrogenation process, and simulation results show that this method can identify the industrial failure condition.

关键词： Failure analysis

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A hybrid differential evolution algorithm integrated with particle swarm optimization 3

A hybrid differential evolution algorithm integrated with pa...

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3rd International Workshop on advanced Computational Intelligence and Intelligent Informatics, IWACIII 2013

作者： Fan, Qinqin Yan, Xuefeng Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education East China University of Science and Technology Shanghai200237 China

In order to implement self-adaptive control parameters, a hybrid differential evolution algorithm integrated with particle swarm optimization (PSODE) is proposed. In PSODE, control parameters are encoded to be a symbiotic individual of original individual, and each original individual has its own symbiotic individual. Differential evolution operators are applied to evolve the original population. And, PSO is applied to co-evolve the symbiotic population. Thus, with the evolution of the original population in PSODE, the symbiotic population is dynamically and self-adaptively adjusted and the real-time optimum control parameters are obtained. To illustrate the performance of PSODE, DE/rand/1, DE/best/1, DE/rand-to-best/1, DE/rand/2, DE/best/2, self-adaptive Pareto DE (SPDE), self-adaptive DE (SDE) and PSODE are applied to optimize 9 benchmark functions. The results show that the average performance of PSODE is the best.

关键词： Particle swarm optimization (PSO)

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A new co-evolutionary immune algorithm for flow shop with zero wait

A new co-evolutionary immune algorithm for flow shop with ze...

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5th FTRA International Conference on Computer Science and its Applications, CSA 2013

作者： Xu, Zhenhao Zhang, Shun Gu, Xingsheng Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai 200237 China

ISBN: (纸本)9783642416736

The flow shop scheduling problems with zero wait is considered as one of the most challenging problems in the field of scheduling. This paper deals with the problem considering the makespan minimization as the objective. In order to solve the corresponding model, a new co-evolutionary immune algorithm (NCIA) is proposed based on the '80/20' rule and a designed local search strategy. Moreover, a global crossover operator is presented to preserve excellent genes fragment and increase the diversity of population during the process of evolution. to confirm the performance of the proposed algorithm, in terms of solution quality, the algorithm is applied to various test instances of benchmark problems available in the literature. The computational results show that the proposed algorithm outperforms other algorithms. © 2014 Springer-Verlag Berlin Heidelberg.

关键词： Scheduling

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Octane model based on principal component analysis and neural network by near-infrared spectroscopy and its application in online gasoline blending process 3

Octane model based on principal component analysis and neura...

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3rd International Workshop on advanced Computational Intelligence and Intelligent Informatics, IWACIII 2013

作者： He, Kaixun Cheng, Hui Qian, Feng Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai200237 China

For the gasoline pipeline blending process, recipe optimization system is greatly dependent on the near-infrared spectroscopy online analyzer, whose spectral model plays an important role in the measurement. The sepectral model's accuracy and adaptability directly affect the applicability of the entire online blending system. This paper studies how to establish model for gasoline octane number for the gasoline pipeline blending process with near-infrared spectroscopy online analyzer. It is proposed using principal component analysis (PCA) together with Artificial Neural Network (ANN) method to establish spectral-model for octane number. Multivariate linear regressions(MLR) and partial least squares (PLS) method have also been used to establish gasoline octane model for comparison purpose. The results show that the model established by PCA and ANN has strong anti-jamming capability and suitable for gasoline online blending application.

关键词： Principal component analysis

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Weld Seam Detection and Feature Extraction Based on Laser Vision 33

Weld Seam Detection and Feature Extraction Based on Laser Vi...

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第三十三届中国控制会议

作者： WANG Xiuping BAI Ruilin LIU Ziteng Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education) Information and Control Experiment Teaching CenterJiangnan University Wuxi Professional College of Science and Technology

ISBN: (纸本)9781479947249

This paper presents an algorithm for real time weld seam detection and feature extraction of butt weld, which is based on laser vision sensor installed on the end hand of welding robot. The algorithm preprocesses the weld seam images captured by laser vision sensor as first step and then detects laser stripe edges thus obtains the center of laser stripe. Then a two-step method is used to extract the initial value of center point of weld seam. Finally the weld region is separated from the laser stripe center line using iterative search method, then two edge points and accurate center point of weld region together with the width of weld seam can be calculated. Experiments have shown that the algorithm proposed in this paper has good stability and accuracy, and it can meet the real-time requirements.

关键词： Welding robot,Laser vision,Seam detection,Feature extraction

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Modeling of batch process based on higher order partial least squares

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Huagong Xuebao/CIESC Journal 2014年第9期65卷 3527-3534页

作者： Wang, Jianping Hu, Yi Shi, Hongbo Key Laboratory of Advanced Control and Optimization for Chemical Process of Ministry of Education East China University of Science and Technology Shanghai200237 China

The products of batch processes are closely related to the daily life of modern people, and it is crucial for batch process monitoring to establish a model based on the normal process data sets. In this paper, according to the characteristics of batch process, a new generalized multilinear regression model, termed the higher order partial least squares (HOPLS), is introduced with the aim to better model the three-way batch data. It differs substantially from the traditional modeling approach, and instead of unfolding the three-way array to a two-way array, it explains the data by a sum of orthogonal Tucker tensors. Higher order orthogonal iteration (HOSVD), tensor transformation and higher order orthogonal iteration (HOOI) are used to find the latent vectors, which contain the maximum information of independent variables and dependent variables simultaneously. And the loading vectors are calculated at the same time. For the new observation values, the dependent variables will be predicted through the output of the model. Finally, the feasibility and efficiency of the method are verified through the PenSim2.0 simulation platform. © All Rights Reserved

关键词： Tensors

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