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6th International Symposium on Neural Networks

作者： Pang, Hali Wei, Yongjun Wang, Shan Liu, Xiaobin Northeastern Univ Coll Informat Sci & Engn Shenyang 110004 Peoples R China

ISBN: (纸本)9783642015069

Considering capacity constraints of manufacturer, a problem of multi-time period production planning Of Supply chain under Customers I demands uncertainty was studied in this paper. According to characteristics of the problem, an optimal stochastic chance constraint programming model with objective of minimum costs was presented, and a real number matrix encoding and stochastic sampling constraints verifying based pso algorithm was proposed. A numeric example is performed, and the results illustrate the feasibility and the effectiveness of proposed model and algorithm.

关键词： Supply chain Multi-time period production plan Demands uncertainty Stochastic chance-constraint programming pso algorithm

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Research on multi time periods procurement plan of supply chain under production demands uncertainty

Research on multi time periods procurement plan of supply ch...

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21st Chinese Control and Decision Conference

作者： Pang Hali Wei Yongjun Chen Caixia Duan Jian NE Univ Shenyang Coll Informat Sci & Engn Liaoning 110004 Peoples R China

ISBN: (纸本)9781424427239

Considering the uncertainty of production demand, a problem of multi time periods procurement plan of supply chain under production demands uncertainty was studied in this paper. According to characteristics of the problem, a real number matrix encoding and stochastic sampling constraints verifying based pso algorithm was proposed. A numeric example is performed, and the results illustrate the feasibility and the effectiveness of proposed model and algorithm.

关键词： supply chain multi time periods procurement plan demands uncertainty stochastic chance-constraint programming pso algorithm

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Gasoline Blending Scheduling Based on Uncertainty

Gasoline Blending Scheduling Based on Uncertainty

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International Conference on Computational Intelligence and Natural Computing

作者： Zhao, Xiaoqiang Wang, Ying Lanzhou Univ Technol Coll Elect & Informat Engn Lanzhou 730050 Gansu Peoples R China

ISBN: (纸本)9780769536453

Blending is an important unit operation in process industry. Gasoline blending scheduling is nonlinear optimization problem with constraints. It is difficult to obtain optimum solution by other general optimization methods. Particle swarm optimization (pso) algorithm is developed for nonlinear optimization problems with both continuous and discrete variables. In order to obtain a global optimum solution quickly, pso algorithm is applied to solve the problem of blending scheduling under uncertainty. The calculation results illustrate that the pso algorithm is valid and effective for the gasoline blending scheduling problem under uncertainty.

关键词： Gasoline Blending scheduling Uncertainty pso algorithm Nonlinear optimization

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Time Series Forecasting for Economic Growth based on Particle Swarm Optimization and Support Vector Machine

Time Series Forecasting for Economic Growth based on Particl...

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3rd International Symposium on Intelligent Information Technology Application

作者： Gang, Long Wuhan Univ Econ & Management Sch Wuhan 430072 Peoples R China

ISBN: (纸本)9780769538594

Economic growth forecasting is important to make the policy on national economic development. Support vector machine (SVM) is a new machine learning method, which seeks to minimize an upper bound of the generalization error instead of the empirical error as in conventional neural networks. In the study, support vector machine and particle swarm optimization is applied in economic growth forecasting, pso is to find the optimal settings of parameters in SVM. The total output value of Xi'an city from 1990 to 2000 was employed to compare the forecasting performances of the proposed PSVM model and RBF neural network forecasting model in economic growth forecasting. The experiment results indicate that the proposed hybrid psoSVM algorithm is better than the RBFNN in economic growth forecasting.

关键词： economic growth time series forecasting support vector machine pso algorithm

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Optimal Location and Capacity of Distributed Generation Based on Scenario Probability

Optimal Location and Capacity of Distributed Generation Base...

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International Conference on Sustainable Power Generation and Supply

作者： Ding Xiaoqun Wu Jiahong Zhao Feng Hohai Univ Dept Elect Engn Nanjing Peoples R China Henan Elect Power Co Henan Peoples R China

ISBN: (纸本)9781424449347

The paper analyzes distributed generation planning of capacity and location. Distributed generation usually installed in distribution system. Distributed generation such as solar generation and wind generation power output is affected by meteorological conditions. DG power production fluctuates frequently, DG injected distribution system is seldom dispatched and controlled by operators. Some control means could not adapt to operation mode frequently varying. Distributed generation planning method must adapt to DG power output random large-scale change. Scenario Probability methodology is applied to take DG optimal planning. The methodology separates random DG output into several portions. Every portion is related to different DG output scenario. Every scenario happening probability is estimated. Planning methods also consider investment cost and power loss of distribution network Technical constraints such as feeder capacity limits, feeder voltage profile are considered. pso algorithm is applied to solve planning problem. Example of IEEE-33 shows that the proposed method is feasible. Planning DG embedded in distribution system can well adapt DG output fluctuation.

关键词： Distributed Generation Distribution network Scenario probability pso algorithm Planning model

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Gasoline Blending Scheduling Based on Uncertainty

Gasoline Blending Scheduling Based on Uncertainty

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2009 International Conference on Computational Intelligence and Natural Computing(CINC 2009)

作者： Xiaoqiang Zhao College of electrical and information engineering,Lanzhou University of Technology,Lanzhou,Gansu 730050,China Ying Wang College of electrical and information engineering,Lanzhou University of Technology,Lanzhou,Gansu 730050,China

Blending is an important unit operation in process industry. Gasoline blending scheduling is nonlinear optimization problem with constraints. It is difficult to obtain optimum solution by other general optimization methods. Particle swarm optimization (pso) algorithm is developed for nonlinear optimization problems with both continuous and discrete variables. In order to obtain a global optimum solution quickly,pso algorithm is applied to solve the problem of blending scheduling under uncertainty. The calculation results illustrate that the pso algorithm is valid and effective for the gasoline blending scheduling problem under uncertainty.

关键词： Gasoline Blending scheduling Uncertainty pso algorithm Nonlinear optimization

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Gait Recognition Method Based on Hybrid Kernel and Optimized Parameter SVM

Gait Recognition Method Based on Hybrid Kernel and Optimized...

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2009 2nd IEEE International Conference on Computer Science and Information Technology（ICCSIT 2009）

作者： Jian Ni College of information and Electronic Engineering Hebei University of Engineering Handan,China,056038 Libo Liang College of information and Electronic Engineering Hebei University of Engineering Handan,China,056038

The gait recognition algorithm adopt support vector machine based on hybrid kernel function and Parameter Optimization,Partial kernel function and overall kernel function are fitted to compose super-kernel function,so that the SVM obtain better generalization ability and generalization ability,In terms of parameter selection,the text uses the objective function and combine OPS algorithm to select the best kernel *** method makes use of the distance of training samples of different classes to find the optimal(or effective) nuclear parameters instead of the standard SVM training *** avoids strong empirical and large amount of calculation of the traditional SVM on model *** the gaits are classified by the support vector machine models,This algorithm is applied to a data-set including thirty *** results demonstrate that the algorithm performs at an encouraging recognition rate and at a relatively lower computational cost.

关键词： gait recognition SVM hybrid kernel objective function pso algorithm

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Research on multi time periods procurement plan of supply chain under production demands uncertainty

Research on multi time periods procurement plan of supply ch...

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2009中国控制与决策会议

作者： Pang Hali,Wei Yongjun,Chen Caixia and Duan Jian College of Information Science & Engineering Northeastern University Shenyang,Liaoning 110004

Considering the uncertainty of production demand,a problem of multi time periods procurement plan of supply chain under production demands uncertainty was studied in this *** to characteristics of the problem,a real number matrix encoding and stochastic sampling constraints verifying based pso algorithm was proposed.A numeric example is performed,and the results illustrate the feasibility and the effectiveness of proposed model and algorithm.

关键词： supply chain multi time periods procurement plan demands uncertainty stochastic chance-constraint programming pso algorithm

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Gait Recognition Method Based on Hybrid Kernel and Optimized Parameter SVM

Gait Recognition Method Based on Hybrid Kernel and Optimized...

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2009 2nd IEEE International Conference on Computer Science and Information Technology(第二届计算机科学与信息技术国际会议 ICCSIT2009)

作者： Jian Ni Libo Liang College of information and Electronic Engineering Hebei University of Engineering Handan China 056038

The gait recognition algorithm adopt support vector machine based on hybrid kernel function and Parameter Optimization. Partial kernel function and overall kernel function are fitted to compose super-kernel function, so that the SVM obtain better generalization ability and generalization ability. In terms of parameter selection, the text uses the objective function and combine OPS algorithm to select the best kernel parameter. This method makes use of the distance of training samples of different classes to find the optimal (or effective) nuclear parameters instead of the standard SVM training samples. It avoids strong empirical and large amount of calculation of the traditional SVM on model selection. Then the gaits are classified by the support vector machine models. This algorithm is applied to a data-set including thirty individuals. Experimental results demonstrate that the algorithm performs at an encouraging recognition rate and at a relatively lower computational cost.

关键词： gait recognition SVM hybrid kernel objective function pso algorithm

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A pso-based Decision Aid for Multi-Aircraft Combat Situations

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INTERNATIONAL JOURNAL OF FUZZY SYSTEMS 2008年第3期10卷 161-167页

作者： Su, Mu-Chun Lai, Shih-Chang Huang, De-Yuan You, Liang-Fu Liao, Yin-Kuei Huang, Jau-Ming Natl Cent Univ Tao Yuan 32001 Taiwan

In this paper, the problem of assigning aircraft to protect assets from attack by enemy aircraft is formulated as a constrained resource allocation problem. The aircraft assignment problem is an NP-complete problem. A pso-based approach incorporating four fuzzy measures of the predominance values for solving the problem is developed to help a military planner reduce the required planning time and provide a better strategy. Several simulations are used to illustrate the effectiveness of the proposed decision aid.

关键词： optimization swarm intelligence pso algorithm genetic algorithm C4ISR

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