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Supply Chain Production-distribution Cost optimization under Grey Fuzzy Uncertainty

Journal of Donghua University(English Edition) 2008年第1期25卷 41-47页

作者：刘东波陈玉娟黄道添玉 College of Mechanical and Electronic Engineering Shanghai Normal University Research Institute of Automation East China University of Science and Technology

Most supply chain programming problems are restricted to the deterministic situations or stochastic environmcnts. Considering twofold uncertainty combining grey and fuzzy factors, this paper proposes a hybrid uncertain programming model to optimize the supply chain production-distribution cost. The programming parameters of the material suppliers, manufacturer, distribution centers, and the customers are integrated into the presented model. On the basis of the chance measure and the credibility of grey fuzzy variable, the grey fuzzy simulation methodology was proposed to generate input-output data for the uncertain functions. The designed neural network can expedite the simulation process after trained from the generated input-output data. The improved particle swarm optimization （PSO） algorithm based on the Differential Evolution （DE） algorithm can optimize the uncertain programming problems. A numerical example was presented to highlight the significance of the uncertain model and the feasibility of the solution strategy.

关键词： supply chain optimization grey fuzzy uncertainty neural netwok particle swarm optimization algorithm differential evolution algorithm

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Power System Stabilization Improvement by Using PSO-based UPFC

Power System Stabilization Improvement by Using PSO-based UP...

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Joint International Conference on Power Systems Technology (POWERCON)/IEEE Power India Conference

作者： Vahidi, B. Jazebi, S. Baghaee, H. R. Tahan, M. Asadi, M. R. Amirkabir Univ Technol Dept Elect Engn Hafez Ave Tehran Iran Univ Tehran Sch Elect & Comp Engn Tehran 14174 Iran

ISBN: (纸本)9781424417636

A robust control strategy is proposed to control UPFC in different operating conditions. PI regulators used for UPFC suffer from the inadequacies of providing suitable control for transient stability enhancement over a wide range of system operating conditions. In this paper an optimization method based on particle-swarm optimization (PSO) algorithm is presented to optimize the parameters of the PI controller of UPFC in order to enhance the power system transient stability. This scheme dispenses the gain dependency of the proportional or integral gains and generates independent control actions. The effectiveness of the proposed method is demonstrated using simulations of a multi-machine power system.

关键词： Inter-area Oscillations particle swarm optimization algorithm Power System Stability UPFC

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Locational marginal pricing prediction in a competitve electrical market using computational intelligence

Locational marginal pricing prediction in a competitve elect...

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Canadian Conference on Electrical and Computer Engineering

作者： Bashir, Z. A. El-Hawary, M. E. Dalhousie Univ Dept Elect & Comp Engn Halifax NS B3J 2X4 Canada

ISBN: (纸本)9781424416424

Deregulation has created a competitive market among power market participants, and the pricing system plays an important role. Locational marginal pricing (LMP) provides clear market signals that identify the locations where power market participants could make their decisions so as to maximize their profits. In this work, artificial neural networks (ANNs) models are used to predict hourly LMP. ANN is trained using the particle swarm optimization (PSO) algorithm. PSO aims to minimize the error function by adjusting neural network's weights and biases using a stochastic optimal search. Wavelet transformed data is fed into neural network as pre-processing stage in order to get a better price pattern that will be reliable for forecasting. The historical LMP and corresponding load demand and temperature are trained, validated and tested over a period of one season. The efficient generalization of proposed model is investigated using early stopping technique. The results were compared with neural models using conventional back-propagation (BP) algorithm and radial basis function (RBF) and yielded encouraging results.

关键词： predicted hourly LMP neural networks wavelet transform particle swarm optimization algorithm

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HPSO-based fuzzy neural network control for AUV

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控制理论与应用（英文版） 2008年第3期6卷 322-326页

作者： Lei ZHANG Yongjie PANG Yumin SU Yannan LIANG School of Naval Architecture Harbin Engineering University Harbin Heilongjiang 150001 China

A fuzzy neural network controller for underwater vehicles has many parameters difficult to tune manually. To reduce the numerous work and subjective uncertainties in manual adjustments, a hybrid particle swarm optimization （HPSO） algorithm based on immune theory and nonlinear decreasing inertia weight （NDIW） strategy is proposed. Owing to the restraint factor and NDIW strategy, an HPSO algorithm can effectively prevent premature convergence and keep balance between global and local searching abilities. Meanwhile, the algorithm maintains the ability of handling multimodal and multidimensional problems. The HPSO algorithm has the fastest convergence velocity and finds the best solutions compared to GA, IGA, and basic PSO algorithm in simulation experiments. Experimental results on the AUV simulation platform show that HPSO-based controllers perform well and have strong abilities against current disturbance. It can thus be concluded that the proposed algorithm is feasible for application to AUVs.

关键词： Autonomous underwater vehicle Fuzzy neural network Model reference adaptive control particle swarm optimization algorithm Immune theory

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Supply China Production-distribution Cost optimization under Grey Fuzzy Uncertainty

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东华大学学报（英文版） 2008年第1期25卷 41-47页

作者： LIU Dong-bo CHEN Yu-juan HUANG Dao TIAN Yu College of Mechanical and Electronic Engineering Shanghai Normal University Shanghai 201418 China Research Institute of Automation East China University of Science and Technology Shanghai 200237 China

Most supply chain programming problems are restricted to the deterministic situations or stochastic environments. Considering twofold uncertainty combining grey and fuzzy factors, this paper proposes a hybrid uncertain programming model to optimize the supply chain production-distribution cost. The programming parameters of the material suppliers, manufacturer, distribution centers, and the customers are integrated into the presented model. On the basis of the chance measure and the credibility of grey fuzzy variable, the grey fuzzy simulation methodology was proposed to generate input-output data for the uncertain functions. The designed neural network can expedite the simulation process after trained from the generated input-output data. The improved particle swarm optimization (PSO) algorithm based on the Differential Evolution (DE) algorithm can optimize the uncertain programming problems. A numerical example was presented to highlight the significance of the uncertain model and the feasibility of the solution strategy.

关键词： supply chain optimization grey fuzzy uncertainty neural network particle swarm optimization algorithm differential evolution algorithm

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Soft Sensor Modeling of Melt-Index of High Pressure Low-Density Polyethylene

Soft Sensor Modeling of Melt-Index of High Pressure Low-Dens...

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20th Chinese Control and Decision Conference

作者： Bu Yan-ping Yu Jin-shou E China Univ Sci & Technol Res Inst Automat Shanghai 200237 Peoples R China

ISBN: (纸本)9781424417339

On the basis of analyzing the particle swarm optimization (PSO) algorithm and support vector machine (SVM), the PSO algorithm with chaos searching is applied to optimize the parameters of SVM, then the PSO-SVM model about a practical soft-sensor of melt-index of High Pressure Low-Density Polyethylene is constructed. The method takes advantages of the minimum structure risk of SVM and the quickly globally optimizing ability of PSO for soft sensor modeling. The simulation results demonstrate that the model has effective generalization performance, higher precision and engineering practicability.

关键词： particle swarm optimization algorithm chaos support vector machine soft-sensor melt-index

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Study on Dynamic Attribute Reduction Based on Improved PSO algorithm

Study on Dynamic Attribute Reduction Based on Improved PSO A...

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7th World Congress on Intelligent Control and Automation

作者： Xia, Kewen Zhang, Ling Wu, Pinghui Zhang, Xinying Hebei Univ Technol Sch Informat Engn Tianjin 300401 Peoples R China

ISBN: (纸本)9781424421138

Attribute reduction is one of the important topics in the research on rough set theory. In confrontation with dynamic data, common methods of attributes reduction have such disadvantages as unstable reduction results, intensive computation and the difficulty in meeting the need of real-time processing. To solve these problems, a method of dynamic attributes reduction with improved PSO algorithm is proposed based on the research of particle swarm optimization. The concrete work is the following: firstly, the traditional PSO algorithm is improved to enhance the global search ability, which increases the diversity of particle population distribution. Secondly, the information decision system data extraction is divided into some subdecision tables, which are reducted by used of improved PSO algorithm. Finally, each reduction results are intersected and get the most minimal reduction. Simulation and experimental results show the dynamic reduction algorithm can overcome the shortcomings of common attribute reduction which possesses the significant effect and rapid computation.

关键词： Rough Set Dynamic Attributes Reduction particle swarm optimization algorithm

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particle swarm optimization fuzzy neural network and its application in soft-sensor modeling of acrylonitrile yield

Particle swarm optimization fuzzy neural network and its app...

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6th International Conference on Machine Learning and Cybernetics

作者： Xu, Yu-Fa Chen, Guo-Chu Yu, Jin-Shou Shanghai DianJi Univ Sch Elect Engn Shanghai 200240 Peoples R China East China Univ Sci & Technol Res Inst Automat Shanghai 200237 Peoples R China

ISBN: (纸本)9781424409723

Firstly, particle swarm optimization fuzzy neural network (PSOFNN) is proposed and the algorithm flow of PSOFNN are given in this paper. Secondly, PSOFNN is applied in soft-sensor modeling of acrylonitrile yield. The new method assumes that fuzzy neural network (FNN) is used to construct the soft-sensor model of acrylonitrile yield and particle swarm optimization algorithm (PSO) is employed to optimize parameters of FNN. Moreover, how to choose the auxiliary variables of soft-sensor is studied carefully. Experiment results show that the model based on PSOFNN has higher precision and better performance than the model based on PSONN. The method proposed by this paper is feasible and effective in soft-sensor of acrylonitrile yield.

关键词： particle swarm optimization algorithm fuzzy neural networks acrylonitrile soft-sensor modelling

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Improvement of particle swarm optimization Based on Neighborhood Cognizance and swarm Decision

Improvement of Particle Swarm Optimization Based on Neighbor...

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3rd International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM 2007)

作者： Zhu Meijie Liu Hanxing Sun Weiwei Zhu TongLin S China Agr Univ Sch Informat Guangzhou 510642 Peoples R China

ISBN: (纸本)9781424413119

The original PSO usually converges prematurely, and falls into the local optimal solution. Aimed at the shortcoming of PSO, this paper put forward Improved PSO based on Neighborhood Cognizance (NCPSO) and Improved NCPSO based on swarm Decision (SDNCPSO). These two improved PSO can reduce the possibility, of converging prematurely. The results of experiment prove that these two improved PSO can improve the performance of global convergence in PSO and make PSO converge to global optimal solution faster.

关键词： Data Mining particle swarm optimization algorithm

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Supply China Production-distribution Cost optimization under Grey Fuzzy Uncertainty

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Journal of Donghua University 2008年第1期000卷

关键词： supply chain optimization grey fuzzy uncertainty neural network particle swarm optimization algorithm differential evolution algorithm

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