检索结果-内蒙古大学图书馆

2007 International Conference on Machine Learning and Cybernetics(IEEE第六届机器学习与控制论国际会议)

作者： YU-FA XU GUO-CHU CHEN JIN-SHOU YU Electric Engineering School Shanghai DianJi University Minhang 200240 Shanghai China Research In Electric Engineering School Shanghai DianJi University Minhang 200240 Shanghai China Research Institute of Automation East China University of Science and Technology Meilong 200237 S

Firstly, particle swarm optimization fuzzy neural network (PSOFNN) is proposed and the algorithm How of PSOFNN are given in this ***, PSOFNN is applied in soft-sensor modeling of acrylonitrile *** 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 ***, how to choose the auxiliary variables of soft-sensor is studied *** results show that the model based on PSOFNN has higher precision and better performance than the model based on *** 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

来源：评论

学校读者我要写书评

暂无评论

Pricing model and algorithm for Online speciality store with consideration of sale association

Pricing model and algorithm for Online speciality store with...

引用

4th International Conference on Service Systems and Service Management

作者： Wang, Junwei Wang, Dingwei Northeastern Univ Sch Informat Sci & Engn Shenyang 110004 Peoples R China

ISBN: (纸本)9781424408849

An online speciality store is a kind of small retail shop on the internet that specializes in some merchandise of the same brand or with the similar function. Putting low prices on some special items is a special pricing strategy in online speciality store, which will increase the flow of the web site and accelerate the sales of other products. The phenomenon that there is positive sale association among different items is called as size effect in this paper. A multi-objective model was built and a particle swarm optimization algorithm was proposed to solve this holistic pricing problem. Maximum of the gross profits and sales was the objective of the model. Simulation results reveal that the model can help online speciality store get more gross profits and more sales.

关键词： online speciality store pricing model size effect particle swarm optimization algorithm multi-objective model

来源：评论

学校读者我要写书评

暂无评论

Real-time DOP ellipsoid in polarization mode dispersion monitoring system by using PSO algorithm

Real-time DOP ellipsoid in polarization mode dispersion moni...

引用

7th International Conference on Computational Science (ICCS 2007)

作者： Zhang, Xiaoguang Duan, Gaoyan Xi, Lixia Beijing Univ Posts & Telecommun Dept Phys Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun Ministry Educ Key Lab Optical Commun & Lightwave Technologies Beijing Peoples R China

ISBN: (纸本)9783540725893

In high bit rate optical fiber communication systems, Polarization mode dispersion (PMD) is one of the main factors to signal distortion and needs to be compensated. PMD monitoring system is the key integral part of an adaptive PMD compensator. The degree of polarization (DOP) ellipsoid obtained by using a polarization scrambler can be used as a feedback signal for automatic PMD compensation. Generally, more than several thousands of sampling data of states of polarization (SOP) must be collected to insure getting a correct DOP ellipsoid. This would result in an unacceptable time consuming for adaptive PMD compensation. In this paper, we introduce the particle swarm optimization (PSO) algorithm in determining the real-time DOP ellipsoid with high precision, requiring only 100 sampling data of SOPs. Experimental results confirm that the PSO algorithm is effective for ellipsoid data fitting with high precision within 250ms using our hardware environment.

关键词： polarization mode dispersion monitoring techniques degree of polarization ellipsoid particle swarm optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Modelisation and optimization of clutch magnet actuator topologies

Modelisation and Optimization of clutch magnet actuator topo...

引用

42nd Annual Meeting of the IEEE-Industry-Applications-Society

作者： Pauvert, V. Bernard, N. Zaim, M. E. Bonnefous, J. IREENA Bd UnivBP406 F-44602 Saint Nazaire France Defontaine SA La Bruffiere France

ISBN: (纸本)9781424412600

This paper presents the study of clutch magnet actuators. Two topologies, considering different magnetic constraints of saturation are designed in order to generate powerful engaging force in a limited volume. The magnetic models are based on a reluctance equivalent magnetic circuit considering the magnet flux leakages. The model results are then compared and validated with finite element computations and measurements on different built actuators. Finally, the validated analytical models are coupled to a particle swarm optimization algorithm in order to maximize the transmitted torque or the torque to volume ratio. The study shows that a reluctance circuit model validated by some experimentation and coupled to a particle swarm algorithm gives a solid and efficient basis for the design and the optimization of clutch magnet actuators.

关键词： Lining clutch analytical modeling magnet actuator leakage flux reluctance equivalent circuit particle swarm optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

MECHANICAL PROPERTY PREDICTION MODEL OF STRIP BASED ON PSO-BP NEURAL NETWORK

MECHANICAL PROPERTY PREDICTION MODEL OF STRIP BASED ON PSO-B...

引用

2007中国宁波国际计算机科学与技术学术大会

作者： Wang Xiaolin Wang Pengfei Liu Hongshen Huang Zhenyi Department of Computer Science & Technology Anhui University of Technology Department of Materials Science & Engineering Anhui University of Technology

ISBN: (纸本)9780972147903

Mechanic property prediction of hot rolled strip has enormous economic benefits and vast applied *** neural network is used almost in Mechanic property prediction of hot rolled strip in *** BP has local infinitesimal *** with PSO(particle swarm optimization) global optimization algorithm,a new PSO-BP neural network is *** PSO-BP algorithm takes on advantages of global optimization ability,the rapid constringency of BP rapid training algorithm,the ability of nonlinear approach of multilayer feedforward network,and improves the performance of Neural ***-BP neural network model has good application foreground.

关键词： Mechanical property prediction particle swarm optimization algorithm BP neural network PSO-BP

来源：评论

学校读者我要写书评

暂无评论

A new support vector machine optimized by improved particle swarm optimization and its application

引用

Journal of Central South University of Technology 2006年第5期13卷 568-572页

作者：李翔杨尚东乞建勋 School of Business Administration North China Electric Power University

A new support vector machine （SVM） optimized by an improved particle swarm optimization （PSO） combined with simulated annealing algorithm （SA） was proposed. By incorporating with the simulated annealing method, the global searching capacity of the particle swarm optimization（SAPSO） was enchanced, and the searching capacity of the particle swarm optimization was studied. Then, the improyed particle swarm optimization algorithm was used to optimize the parameters of SVM （c,σ and ε）. Based on the operational data provided by a regional power grid in north China, the method was used in the actual short term load forecasting. The results show that compared to the PSO-SVM and the traditional SVM, the average time of the proposed method in the experimental process reduces by 11.6 s and 31.1 s, and the precision of the proposed method increases by 1.24% and 3.18%, respectively. So, the improved method is better than the PSO-SVM and the traditional SVM.

关键词： support vector machine particle swarm optimization algorithm short-term load forecasting simulated annealing

来源：评论

学校读者我要写书评

暂无评论

Hardware software partitioning using particle swarm optimization technique

Hardware software partitioning using particle swarm optimiza...

引用

6th International Workshop on System-on-Chip for Real-Time Applications

作者： Abdelhalim, M. B. Salama, A. E. Habib, S. E. -D. Cairo Univ Fac Engn Giza 12211 Egypt

ISBN: (纸本)9781424408986

In this paper we investigate the application of the particle swarm optimization (PSO) technique for solving the Hardware/Software partitioning problem. The PSO is attractive for the Hardware/Software partitioning problem as it offers reasonable coverage of the design space together with O(n) main loop's execution time, where n is the number of proposed solutions that will evolve to provide the final solution. We carried out several tests on a hypothetical, relatively-large Hardware/Software partitioning problem using the PSO algorithm as well as the Genetic algorithm (GA), which is another evolutionary technique. We found that PSO outperforms GA in the cost function and the execution time. For the case of unconstrained design problem, we tested several hybrid combinations of PSO and GA algorithms;including PSO then GA, GA then PSO, GA followed by GA, and finally PSO followed by PSO. We found that a PSO followed by GA algorithm gives small or no improvement at all, while a GA then PSO algorithm gives the same results as the PSO alone. The PSO algorithm followed by another PSO round gave the best result as it allows another round of domain exploration. The second PSO round assign new randomized velocities to the particles, while keeping best particle positions obtained in the first round. We propose to name this successive PSO algorithm as the Re-excited PSO algorithm.

关键词： embedded systems hardware/software co-design hardware/software partitioning particle swarm optimization algorithm genetic algorithm evolutionary algorithms re-excited PSO

来源：评论

学校读者我要写书评

暂无评论

An improved particle swarm optimization method based on borderline search strategy for transmission network expansion planning

An improved particle swarm optimization method based on bord...

引用

5th International Conference on Machine Learning and Cybernetics

作者： Niu, Dong-Xiao Ling, Yun-Peng Zhao, Qi Zhao, Qing-Ying North China Elect Power Univ Sch Business & Adm Baoding 071002 Peoples R China

ISBN: (纸本)1424400600

To solve the problem of network expansion, an improved particle swarm optimization algorithm (PSO) is proposed in this paper. This method initialized the particle swarm according to borderline search mind, made the initialized particle near the safety line, overcome the defect of the uncertainty in the rational distribution of particle initialization, optimizing the range of the initialization. Numerical simulation results of power transmission network planning demonstrate the feasibility and efficiency of this method, and shed new light on the further improving of PSO.

关键词： transmission network expansion planning particle swarm optimization algorithm borderline search strategy

来源：评论

学校读者我要写书评

暂无评论

Forecasting of ozone level in time series using MLP model with a novel hybrid training algorithm

引用

ATMOSPHERIC ENVIRONMENT 2006年第5期40卷 913-924页

作者： Wang, D Lu, WZ City Univ Hong Kong Dept Bldg & Construct Kowloon Hong Kong Peoples R China

As far as the impact of tropospheric ozone (O-3) On human heath and plant life are concerned, forecasting its daily maximum level is of great importance in Hong Kong as well as other metropolises in the world. This paper proposed a multi-layer perceptron (MLP) model with a novel hybrid training method to perform the forecasting task. The training method synergistically couples a stochastic particle swarm optimization (PSO) algorithm and a deterministic Levenberg-Marquardt (LM) algorithm, which aims at exploiting the advantage of both. The performance of such a hybrid model is further compared with ones obtained by the MLP model trained individually by these two training methods mentioned above. Based on original data collected from two typical monitoring sites with different O-3 formation and transportation mechanism, the simulation results show that the hybrid model is more robust and efficient than the other two models by not only producing good results during non-episodes but also providing better consistency with the original data during episodes. (c) 2005 Elsevier Ltd. All rights reserved.

关键词： daily maximum ozone level multi-layer perceptron model particle swarm optimization algorithm Levenberg-Marquardt algorithm performance comparison

来源：评论

学校读者我要写书评

暂无评论

Development and evaluation of decision-making model for stock markets

引用

JOURNAL OF GLOBAL optimization 2006年第1期36卷 1-19页

作者： Nenortaite, Jovita Simutis, Rimvydas Vilnius Univ Kaunas Fac Hujmanities LT-44280 Kaunas Lithuania

The paper introduces an intelligent decision-making model which is based on the application of artificial neural networks (ANN) and swarm intelligence technologies. The proposed model is used to generate one-step forward investment decisions for stock markets. The ANN are used to make the analysis of daily stock returns and to calculate one day forward decision for purchase of the stocks. Subsequently the particle swarm optimization (PSO) algorithm is applied in order to select the "the best" ANN for the future investment decisions and to adapt the weights of other networks towards the weights of the best network. The experimental investigations were made considering different forms of decision-making model: different number of ANN, ANN inputs, sliding windows, and commission fees. The paper introduces the decision-making model, its evaluation results and discusses its application possibilities.

关键词： decision-making model particle swarm optimization algorithm stock markets

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：