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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probability while ignoring the effect of the other probabilities,the traditional method of probability distribution has been replaced by the level characteristics value of fuzzy set theory;further more particle swarm optimization algorithm has been employed to optimize the coefcient of level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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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（ICCSIT 2009）

作者： WenJie Tian Automation Institute Beijing Union University,Beijing,China Yue Tian Finance Institute Capital University of Economics and Business Beijing,China Lan Ai Automation Institute Beijing Union University,Beijing,China JiCheng Liu Automation Institute Beijing Union University,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 *** 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 ***,the proposed PSOA-SVR model that can automatically determine the optimal parameters was tested on the prediction of electronic circuit ***,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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Dynamic Compensation for Infrared Thermometer Based on Wiener Model and particle swarm optimization algorithm

Dynamic Compensation for Infrared Thermometer Based on Wiene...

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

A novel structure of dynamic model is proposed in this paper and applied to construct a dynamic model to correct the dynamic errors of the infrared thermometer,because of which the dynamic performance of the thermometer is effectively *** dynamic compensator is established and the compensation is described and explicated by the Wiener *** to Wiener model,the novel structure is *** identification of thermometer non-linear dynamic compensator is achieved by particle swarm optimization *** results show that the stabilizing time of the thermometer is reduced less than 7 ms from 26 ms and the dynamic performance is obviously improved after compensation.

关键词： Wiener model compensation particle swarm optimization algorithm infrared thermometer identification

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Study on Passenger Train Stopping Scheme Based on Improved particle swarm optimization algorithm

Study on Passenger Train Stopping Scheme Based on Improved P...

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2009 IEEE International Conference on Intelligent Computing and Intelligent Systems(ICIS2009)

作者： Shuang Wang,Peng Zhao,Ke Qiao School of Traffic and Transportation Beijing Jiaotong University Beijing,P.R.China

This paper develops a multi-objective optimization model for the passenger train stopping scheme on high-speed railway *** the stopping times for all passenger trains,minimizing travel distance of empty trains and minimizing the number of transfer passengers are the three planning objectives of the *** a given travel demand and specified capacity of stops,the model is solved by heuristic *** improved discrete particle swarm optimization(PSO) algorithm is presented to determine the best-compromise train stopping scheme with high effectiveness and *** the algorithm,a stop based representation is designed,and a new method is used to update the position and velocity of *** order to keep the particle swarm algorithm from premature stagnation,the simulated annealing algorithm,which has local search ability,is combined with the PSO algorithm to make elaborate search near the optimal solution,then the quality of solutions is improved *** empirical study on a given small railway network is conducted to demonstrate the effectiveness of the model and the performance of the *** experimental results show that the hybrid algorithm has great advantages in both success rate and convergence speed compared with other discrete PSO algorithm and genetic algorithm,and an optimal set of stopping schemes can always be generated for a given *** achieve the best planning outcome,the stopping schemes should be flexibly planned,and not constrained by specific ones as often set by the planner.

关键词： high-speed railway lines passenger train stopping scheme multi-objective programming particle swarm optimization algorithm simulated annealing algorithm

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Economic and environmental optimal operation of isolated multi-element complementary microgrid based on PSO with adaptive mutation

Economic and environmental optimal operation of isolated mul...

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2015 3rd International Conference on Machinery,Materials and Information Technology Applications(ICMMITA 2015)

作者： Zhu Weiping Yan Shuchang Wang Yulong Li Peng Han Pengfei Jiangsu Electric Power Company Research Institute State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University

Standalone multi-element complementary microgrid plays a very important role in solving the electricity problems of many areas, which are rich in renewable energy but have problems in the power supply of traditional power grid. To ensure the reliability of power supply while improving overall economic and environmental operation in micro-grid system, we have to optimize the operation of the system according to actual conditions of the system. This paper considered operating costs of the system and gas pollution emissions, established an optimization model. And the dispatch strategies are designed. Finally, utilized adaptive mutation particle swarm optimization algorithm to solve operational problems. Specific case study results verified the reasonableness and effectiveness of the algorithm.

关键词： isolated multi-element complementary microgrid economic and environmental operation particle swarm optimization algorithm adaptive mutation

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Research on Path optimization with PSO for Unmanned Vehicle

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INTERNATIONAL JOURNAL OF ONLINE ENGINEERING 2015年第8期11卷 21-24页

作者： Cai, Li Jia, Jianping Lei, Juan Wuhan Donghu Univ Wuhan 430070 Peoples R China

The development of autonomous unmanned vehicles is of high interest to many organizations around the world and path planning is the key point of the navigation for the autonomous unmanned vehicle. Intelligent algorithms have been applied in this field and an essential aspect of unmanned vehicles autonomy is the ability for automatic path planning. In this paper, particle swarm optimization algorithm as one of new swarm intelligent optimization methods is introduced into a path planning for autonomous vehicle, which is constructed of a particle representation methods for vehicle routing problem with fast convergence speed. The results show that the particle swarm optimization algorithm can obtain the solution of the vehicle routing problem quickly and effectively. It is a good method for solving the vehicle routing problem.

关键词： Path optimization particle swarm optimization algorithm Wheeled vehicle

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Optimal Design of Power System stabilizer in a Multi-machine System Using Evolutionary Computing, A Comparative Study

Optimal Design of Power System stabilizer in a Multi-machine...

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IEEE Power, Communication and Information Technology Conference

作者： Niranjan Nayak Soumya Pradhan Sangram Keshari Rout ray Department of EICE Institute of Technical Education & Research Siksha 'O'Anusandhan University Department of EE. Institute of Technical Education & Research Siksha 'O'Anusandhan University Department of EEE Institute of Technical Education & Research Siksha 'O'Anusandhan University

ISBN: (纸本)9781479974566

When the power system subjected to disturbances, stability is a challenging task. The Power System Stabilizer plays an important role to reduce the low frequency oscillation (LFO) and enhance the performance. This is because, the PSS is low cost, flexible and easy to implement. In this work the problem is designed as a single objective optimization technique to tune the PSS parameters with Eigenvalue analysis. Here the Invasive Weed optimization algorithm which is found suitable for these types of problems is selected as a tool to find optimal solutions. Simulations are executed on a 4-machine power system for different operating conditions such as heavy load, light load and capacitive load. The results are obtained under different fault conditions with Invasive Weed optimization technique and compared the same with PSO. At last it is observed that the IWO technique performs better in damping overshoot and settling time.

关键词： particle swarm optimization algorithm Invasive weed optimization technique Low frequency oscillation Stability of Power system

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Distributed Generation planning Research based on Timing Characteristics

Distributed Generation planning Research based on Timing Cha...

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2015 4th International Conference on Computer, Mechatronics, Control and Electronic Engineering (ICCMCEE 2015)

作者： Haifeng Su Mengjin Hu Zhirui Liang School of North China Electric Power University

ISBN: (纸本)9781510813465

In this paper, considering the load, wind power and photo voltaic timing characteristics, stablishes a planning model containing distributed power energy storage device. In the planning model, the minimization of the total cost including the initial investment, fuel costs, net loss costs, environmental damage costs, operation and maintenance costs and power purchase costs. To improve the acceptance ability of the distribution network, and to ensure the economy. Then, using the particle swarm optimization algorithm to solve a typical example, to show that the model and the proposed method is correct and effective.

关键词： distributed generation capacity determination particle swarm optimization algorithm

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