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International Conference on Electrical Machines and Systems

作者： Giuseppe Marsala Antonella Ragusa I.S.S.I.A - C.N.R. U.O.S. Palermo- Italy

ISBN: (纸本)9781467388634

The paper presents a constrained optimization procedure to design a DC-DC converter with coupled inductors for minimizing the power losses. Two algorithms have been used, Genetic and particle swarm optimization algorithm, and the results have been compared. In particular, with the proposed technique, the electrical, magnetic and geometrical characteristics of the coupled inductors have been obtained and the values of duty cycle and frequency have been defined in order to obtain the maximum efficiency.

关键词： DC-DC converter Genetic algorithm particle swarm optimization algorithm power losses

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DNA SEQUENCE SETS DESIGN BY particle swarm optimization algorithm

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INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL 2009年第8期5卷 2249-2255页

作者： Zhang, Qiang Zhang, Rui Wang, Bin Dalian Univ Liaoning Key Lab Intelligent Informat Proc Dalian 116622 Peoples R China

Designing DNA sequence sets is a fundamental issue in the fields of nanotechnology and nanocomputing. Not only quality but also quantity of DNA coding sequences affect the reliability of DNA computing. For this reason, many researchers have paid their attentions to find more and better DNA sequences in DNA computing. In this paper we present particle swarm optimization algorithm (PSO) for the design of DNA sequence sets, namely sets of equal-length sequence over the nucleotides alphabet A,C,G,T that satisfy H-distance constraint. In our computational experiments, we succeed in generating better sequences sets. We give some practical values which satisfy H-distance constraint, and it has some directions for the theoretical lower bound in DNA computing.

关键词： DNA sequence sets particle swarm optimization algorithm H-distance

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Joint Multiuser And Inter-symbol Interference Suppression In CDMA Systems Using particle swarm optimization algorithms

Joint Multiuser And Inter-symbol Interference Suppression In...

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21st Iranian Conference on Electrical Engineering (ICEE)

作者： Arani, Atefeh Haji Jamali Azmi, Paeiz Tarbiat Modares Univ Tehran Iran

ISBN: (纸本)9781467356343

Using particle swarm optimization (PSO) has become a common heuristic technique in many fields of engineering. In this paper, we apply PSO to solve Joint Multiuser and Inter-symbol Interference (ISI) Suppression problems in the code-division multiple-access (CDMA) systems over multipath Rayleigh fading channel, to reduce the computational complexity. In the proposed method, conventional detector (CD) is used as the first stage to initialize the PSO algorithm and time-varying acceleration coefficients (TVAC) are used in PSO algorithm. The simulation results show that the performance of PSO-based MUD with TVAC is promising and outperform the CD.

关键词： Code division multiple access particle swarm optimization algorithm multiple access interference Inter-symbol Interference multiuser detection

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Multi_objective Model of Mixed Continuous/Batch Plants Planning Problem and Its particle swarm optimization algorithm

Multi_objective Model of Mixed Continuous/Batch Plants Plann...

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21st Chinese Control and Decision Conference

作者： Xu Xinli Dai Xinhua Hao Ping Wang Wanliang Zhejiang Univ Technol Coll Informat Engn Hangzhou 310014 Zhejiang Peoples R China Zhejiang Univ Technol Software Coll Hangzhou 310014 Zhejiang Peoples R China

ISBN: (纸本)9781424427239

A multi-objective, multi-level, multi-product, multi-constraint batch planning problem is abstracted from production of diaphragm caustic soda, and the problem is formulated as a mathematic optimization model with constraints involved resources, work manufacture processes and production capacity etc. Some sub_objectives were considered in the problem model, such as total profit amount, profit margin, total energy wastage and wastage per ten thousand RMB.A modified particle swarm optimization algorithm with a percent-coding and dynamic-bounds coding scheme is proposed for the problem. The validity and flexibility of the model and algorithm are verified by calculating the data from production practices numerically.

关键词： Medium-term Planning Problem particle swarm optimization algorithm Multi_objective

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Research on a Distribution Center Location Model Based on a particle swarm optimization algorithm

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International Journal of Plant Engineering and Management 2009年第3期14卷 151-157页

作者： WANG Fei 1,2,HU Xin-bu3,JIA Tao41 School of Economy and Management,Chang’an University,Xi’an 710064,P.R.China 2 Institute of Human Geography,Xi’an International Studies University,Xi’an 710061,P.R.China 3 Architecture Engineering Department,Engineering College of Armed Police Forces,Xi’an 710086,P.R.China 4 School of Management,Xi’an Jiaotong University,Xi’an 710069,P.R.China School of Economy and Management Chang'an UniversityXi'an 710064P.R.China Institute of Human Geography Xi'an International Studies UniversityXi'an 710061P.R.China Architecture Engineering Department Engineering College of Armed Police ForcesXi'an 710086P.R.China School of Management Xi'an Jiaotong UniversityXi'an 710069P.R.China

Logistics is supposed to be the important source of profits for the enterprises besides reducing material consumption and improving labor productivity. Transportation costs, distribution center construction costs, ordering costs, safe inventory costs and inventory holding costs are the important parts of the total logistics costs. In this paper, based on the research results of LMRP（ location model of risk pooling） location with fixed construction cost, the LMRPVCC （ location model of risk pooling based on variable construction cost） will be introduced. Applying particle swarm optimization to several computational instances, the authors find the suboptimum solution of the model.

关键词： location-inventory particle swarm optimization algorithm variable cost of construction

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STUDY ON SATELLITE BROADCASTING SCHEDULING BASED ON particle swarm optimization algorithm

STUDY ON SATELLITE BROADCASTING SCHEDULING BASED ON PARTICLE...

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2009 IEEE International Conference on Communication Technology and Applications（ICCTA2009）

作者： Kewen Xia~1,Fei Zheng~2,Yue Chi,Rui Wu School of Information Engineering, Hebei University of Technology,Tianjin 300401,China

The efficiency of utilizing the satellite communications resource and system can be improved by optimizing the satellite broadcasting scheduling with genetic *** drawbacks such as complicated genetic operation, tardy convergent speed and the aptness to sink into local minimum within the Genetic algorithm(GA) have encouraged a satellite broadcasting scheduling approach for resolving the scheduling *** approach was based on the particle swarm optimization(PSO) algorithm which involved in processes such as constructing the model of satellite broadcasting scheduling,initialization of the particles and particle *** has been shown by simulation analysis that satellite broadcasting scheduling based on the PSO algorithm was feasible and its optimization result was significant.

关键词： Satellite broadcasting particle swarm optimization algorithm Genetic algorithm Simulation analysis

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AN IMPROVED NEURAL NETWORK ADAPTIVE SLIDING MODE CONTROL USED IN ROBOT TRAJECTORY TRACKING CONTROL

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INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL 2015年第5期11卷 1655-1666页

作者： Liu, Tianhua Yin, Shoulin Shenyang Normal Univ Software Coll 253 North Huanghe St Shenyang 110034 Liaoning Peoples R China

This paper proposes an improved neural network adaptive sliding mode control method based on the neural network sliding mode control to improve the performance of the robot trajectory tracking control. This new scheme regards neural network as a controller and adopts robust control law to eliminate the approximation error which uses its nonlinear mapping ability to approximate various unknown nonlinear systems. Hidden layer units and network structure parameter have an effect on neural network mapping. So we will decrease chattering. We conduct experiments with three joints robot and give an example to verify this paper's scheme under the MATLAB platform. The results of experiments show that the new neural network adaptive sliding mode control has a good control accuracy and robustness than other control methods. The new scheme reduces the chattering effectively and also decreases the effect of hidden layer units and network structure parameter on neural network mapping. It will be a good choice for the robot trajectory tracking control.

关键词： Robot tracking Neural network Neural network adaptive sliding mode control particle swarm optimization algorithm Chattering

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Higher-order Multivariate Markov Chains based on particle swarm optimization algorithm for Air Pollution Forecasting

Higher-order Multivariate Markov Chains based on Particle Sw...

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2009 Asia-Pacific Conference on Information Processing

作者： Zhilong Wang,Zengtai Gong,Weigang Zhao and Wenjin Zhu Dept.of Basic Courses,Lanzhou Polytechnic College,Lanzhou 730050 College of Math.& Information Science,Northwest Normal University,Lanzhou 730070 School of Mathematics & Statistics,Lanzhou University,Lanzhou 730000

This paper presents a higher-order multivari-ate Markov chain model combined with particle swarm optimization *** to some deciencies,such as only considering the maximum probability while ignoring the effect of the other probabilities,the traditional method of probability distribution has been replaced by the level characteristics value of fuzzy set theory;further more particle swarm optimization algorithm has been employed to optimize the coefcient of level characteristics *** recent years,air pollution acutely aggravates chronic diseases in mankind,such as sulfur dioxide pollution which plays a most important role in acid *** order to confront air pollution problems and to plan abatement strategies,both the scientic community and the relevant authorities have focused on monitoring and analyzing the atmospheric pollutants *** the forecast of air pollutants as a case,we illustrate the improvement of accuracy and efciency of the new method and the result shows the new method is predominant in forecasting of multivariate and non-linear data.

关键词： Mean-Standard deviation division method Higher-order multivariate Markov chain Level characteristics value particle swarm optimization algorithm

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Support Vector Regression and particle swarm optimization algorithm for Intelligent Electronic Circuit Fault Diagnosis

Support Vector Regression and Particle Swarm Optimization Al...

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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： WenJie Tian Lan Ai JiCheng Liu Yue Tian Automation Institute Beijing Union University Beijing China Finance Institute Capital University of Economics and Business Beijing China

In the analysis of electronic circuit fault diagnosis based on support vector regression (SVR), irrelevant or correlated features in the samples could spoil the performance of the SVR classifier, leading to decrease of prediction accuracy. In order to solve the problems mentioned above, this paper used rough sets as a preprocessor of SVR to select a subset of input variables and employed the particle swarm optimization algorithm (PSOA) to optimize the parameters of SVR. Additionally, the proposed PSOA-SVR model that can automatically determine the optimal parameters was tested on the prediction of electronic circuit fault. Then, we compared the proposed PSOA-SVR model with other artificial intelligence models of (BPN and fix-SVR). The experiment indicates that the proposed method is quite effective and ubiquitous.

关键词： fault diagnosis rough set particle swarm optimization algorithm support vector regression electronic circuit

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Biochemical systems identification by a random drift particle swarm optimization approach

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BMC BIOINFORMATICS 2014年第6-Sup期15卷 1-17页

作者： Sun, Jun Palade, Vasile Cai, Yujie Fang, Wei Wu, Xiaojun Jiangnan Univ Minist Educ Key Lab Adv Proc Control Light Ind Wuxi 214122 Jiangsu Peoples R China Coventry Univ Fac Engn & Comp Coventry CV1 5FB W Midlands England Jiangnan Univ Key Lab Ind Technol Sch Biotechnol Wuxi 214122 Jiangsu Peoples R China

Background: Finding an efficient method to solve the parameter estimation problem (inverse problem) for nonlinear biochemical dynamical systems could help promote the functional understanding at the system level for signalling pathways. The problem is stated as a data-driven nonlinear regression problem, which is converted into a nonlinear programming problem with many nonlinear differential and algebraic constraints. Due to the typical ill conditioning and multimodality nature of the problem, it is in general difficult for gradient-based local optimization methods to obtain satisfactory solutions. To surmount this limitation, many stochastic optimization methods have been employed to find the global solution of the problem. Results: This paper presents an effective search strategy for a particle swarm optimization (PSO) algorithm that enhances the ability of the algorithm for estimating the parameters of complex dynamic biochemical pathways. The proposed algorithm is a new variant of random drift particle swarm optimization (RDPSO), which is used to solve the above mentioned inverse problem and compared with other well known stochastic optimization methods. Two case studies on estimating the parameters of two nonlinear biochemical dynamic models have been taken as benchmarks, under both the noise-free and noisy simulation data scenarios. Conclusions: The experimental results show that the novel variant of RDPSO algorithm is able to successfully solve the problem and obtain solutions of better quality than other global optimization methods used for finding the solution to the inverse problems in this study.

关键词： Inverse Problem particle swarm optimization Local Search Differential Evolution particle swarm optimization algorithm

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