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4th International Conference on Manufacturing Science and Engineering (ICMSE 2013)

作者： Zhang, Yingjie Xu, Jianxing Civil Aviat Univ China Coll Aeronaut Automat Tianjin Peoples R China Civil Aviat Univ China Chaoyang flight Coll Tianjin Peoples R China

ISBN: (纸本)9783037856932

Due to the complex relations among the various factors, the nonlinear calculation of aircraft fuel consumption is very difficult. The purpose of this paper is to present a simplified method to estimate aircraft fuel consumption using a novel particle swarm neural network. Fuel consumption information obtained directly from QAR recorded flight data is trained by the neural network. The method can avoid the high cost of flight testing and wind tunnel testing. An improved particle swarm optimization algorithm embeds neural network topology to replace the network BP learning algorithm. The experimental results demonstrate that the proposed method integrates a new particle swarm neural network system, and significantly improves the system's learning ability and prediction of evolutionary effects.

关键词： BP neural network particle swarm optimization algorithm Fuel consumption QAR

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Research and Simulation on RBFNN Optimized by particle swarm algorithm

Research and Simulation on RBFNN Optimized by Particle Swarm...

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International Conference on Sensors, Measurement and Intelligent Materials (ICSMIM 2012)

作者： Lu, Jinna Bai, Yanping North Univ China Mailbox 7223 Xueyuan RD Taiyuan 030051 Shanxi Prov Peoples R China North Univ China Dept Math Taiyuan 030051 Shanxi Prov Peoples R China

ISBN: (纸本)9783037856529

RBF neural network with the parameters randomly selected may have unstabilized error. hi this paper on this problem we improve the RBFNN by introducing particle swarm optimization algorithm, and use the algorithm of searching optimization process to adjust the three parameters of RBFNN. Through simulating four groups of test data by improved PSO-RBFNN network, we get optimal parameters. The simulation shows that the error of the improved network is smaller.

关键词： Radial Basis Function particle swarm optimization algorithm optimization simulation

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Improved Harmony Search algorithm with Crossover Operation

Improved Harmony Search Algorithm with Crossover Operation

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2nd International Conference on Automatic Control and Mechatronic Engineering (ICACME2013)

作者： Xia, Honggang Wang, Qingliang Shenyang Univ Informat Engn Coll Shenyang 110004 Peoples R China Northeastern Univ Informat & Sci Coll Shenyang 110004 Peoples R China Liaoyang Construct Vocat Coll Liaoyang 111003 Peoples R China

ISBN: (纸本)9783037858653

Harmony search (HS) algorithm is a good meta-heuristic intelligent optimization method and it does depend on imitating the music improvisation process to generate a perfect state of harmony. However, intelligent optimization methods is easily trapped into local optimal, HS is no exception. In order to improve the performance of HS, a new variant of harmony search algorithm is proposed in this paper. The variant introduce a new crossover operation into HS, and design a strategy to adjust parameter pitch adjusting rate (PAR) and bandwidth (BW). Several standard benchmarks carried out to be tested. The numerical results demonstrated that the superiority of the proposed method to the HS and recently developed variants (IHS, and GHS).

关键词： Harmony search algorithm position updating particle swarm optimization algorithm adjust

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The Available Transfer Capability Based on a Chaos Cloud particle swarm algorithm

The Available Transfer Capability Based on a Chaos Cloud Par...

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9th International Conference on Natural Computation (ICNC)

作者： Su, Hongsheng Qi, Ying Song, Xi Lanzhou Jiaotong Univ Inst Automat & Elect Engn Lanzhou 730070 Peoples R China Gansu Prov Elect Power Co Informat & Commun Technol Co Lanzhou Peoples R China

ISBN: (纸本)9781467347143

An optimal power flow model was established for Available Transfer Capability (ATC) under the static security constraints. The maximum active power of all load nodes in receiving area was taken as objective function. To aim at the low accuracy and premature convergent in ATC optimization algorithms, the chaos cloud particle swarm algorithm based on golden section evaluation criteria (CCGPSO) was proposed. This method divided the particle swarm into standard particle, chaos cloud particle and cloud particle, which used the golden section judge principle according to fitness level. Every sub-swarm particle had respective different algorithm operations. The ATC calculated by the proposed algorithm was simulated in the IEEE-30 node test system. Results are compared with the cloud PSO and chaos PSO algorithm. The simulation results verify that the CCGPSO is greatly superior to the cloud PSO and chaos PSO in terms of accuracy and speed. It is more suitable for solving such large-scale non-linear multi-constraint optimization problems.

关键词： Available Transfer Capability(ATC) golden section particle swarm optimization algorithm

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Tolerance Analog Circuit Hard Fault and Soft Fault Diagnosis Based on particle swarm Neural Network

Tolerance Analog Circuit Hard Fault and Soft Fault Diagnosis...

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4th International Conference on Manufacturing Science and Engineering (ICMSE 2013)

作者： Han, Baoru Li, Jingbing Wu, Hengyu Hainan Software Profess Inst Dept Elect Engn Qionghai Peoples R China Hainan Univ Coll Informat Sci & Technol Haikou Hainan Peoples R China

ISBN: (纸本)9783037857243

This paper presents a tolerance analog circuit hard fault and soft fault diagnosis method based on the BP neural network and particle swarm optimization algorithm. First, select the mean square error function of BP neural network as the fitness function of the PSO algorithm. Second, change the guidance of neural network algorithms rely on gradient information to adjust the network weights and threshold methods, through the use of the characteristics of the particle swarm algorithm group's parallel search to find more appropriate network weights and threshold. Then using the adaptive learning rate and momentum BP algorithm to train the BP neural network. Finally, the network is applied to fault diagnosis of analog circuit, can quickly and effectively to the circuit fault diagnosis.

关键词： Fault diagnosis Tolerance analog circuits particle swarm optimization algorithm BP neural network

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Cellular particle swarm optimization algorithm for Logistics Distribution Locating Problem

Cellular Particle Swarm Optimization Algorithm for Logistics...

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2008 International Seminar on Future BioMedical Information Engineering(FBIE 2008)

作者： Yi Liu Institute of Management Science& Information Engineering,Hangzhou Dianzi University,zhejiang 310018,China

Logistics distribution locating problem is an important area in Logistics,which select the most reasonable location of distribution centers from many *** paper establish the Cellular PSO algorithm,which combine the particle swarm optimization algorithm and cellular *** algorithm was tested in the simulation experiment,and the result indicate that the Cellular PSO algorithm is a effective method of solving the problem of choosing the distribution centers location which can overcome the low precision of the basic particle swarm optimization *** additional,the Cellular PSO algorithm has high quality and efficiency of searching.

关键词： Cellular Automata particle swarm optimization algorithm ,Logistics Distribution Centers Location

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The Application of An Improved Cultural algorithm in Grid Computing

The Application of An Improved Cultural Algorithm in Grid Co...

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第25届中国控制与决策会议

作者： ZHOU Wei BU Yan-ping ZHOU Ye-qing School of Business East China University of Science and Technology School of Technology Shanghai Jiaotong University School of Mathematical sciences Fudan University

ISBN: (纸本)9781467355339

A cultural algorithm, rooting from simulation of evolution of human being society, provides a new computable framework of evolution algorithms. A novel cultural algorithm based on particle swarm optimization （PSO） algorithm was proposed in this paper. With the advent of the grid, task scheduling in heterogeneous environments becomes more and more important. After analyzing the model of grid scheduling problem, the CPSO algorithm was presented to solve the resource scheduling problem in grid computing. The optimal objective is to minimize the total completing time. The improved algorithm can keep all the advantages of the standard PSO, such as implementation simplicity, low computational burden, and few control parameters, etc. Simulation results demonstrate that it can be superior to the regular PSO. We also tested the CPSO algorithm with the Max-Min method to show the algorithm’s efficiency.

关键词： cultural algorithm particle swarm optimization algorithm grid task scheduling

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A new control system design for a small hydro-power plant based on particle swarm optimization-fuzzy sliding mode controller with Kalman estimator

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 2012年第4期34卷 388-400页

作者： Zargari, A. Hooshmand, R. Ataei, M. Univ Isfahan Dept Elect Engn Esfahan Iran

One of the main problems in small hydro-power plants that are locally used is their frequency control system. In this paper, a suggested control system based on the fuzzy sliding mode controller is presented for controlling the network frequency. Also, the proposed control strategy is compared with a PI controller and conventional sliding mode controller. In order to regulate the membership functions of fuzzy system more accurately, the particle swarm optimization algorithm is also applied. Moreover, because of unavailability of the control system variables, an estimator is suggested for estimating and identifying the system variables. This estimator will reduce the costs of implementing the control method. The simulation results show the ability of controller system in controlling the local network frequency in the presence of load and parameter's variations.

关键词： Fuzzy control Kalman estimator particle swarm optimization algorithm sliding mode control small hydro-power plant

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Optimal Relay Placement in Microgrids Considering Critical Clearing Time

Optimal Relay Placement in Microgrids Considering Critical C...

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IEEE 7th International Power Engineering and optimization Conference (PEOCO)

作者： Mirazimi, S. J. Salehi, B. Tadayon, M. Karshenas, H. R. Univ Southern Queensland Fac Engn & Surveying Toowoomba Qld 4350 Australia Isfahan Univ Dept Elect & Comp Engn Esfahan Iran Distribut Network Dept Behrad Consulting Engn Esfahan Iran

ISBN: (纸本)9781467350747;9781467350723

This paper is concerned with the optimal placement of protection devices in a microgrid using particle swarm optimization algorithm. One of the main advantages of Distributed Generation (DG) scheme and microgrids in modern distribution systems is the reduction of number of outages and the associated damages caused by them. This task is accomplished by supplying a feeder from multiple sources. In order to prevent generator instability in DGs connected to utility, it is necessary to improve the protective schemes of traditional distribution systems and also to use proper relaying and setting for DGs. All of the downstream overcurrent (OC) relays of each DG are coordinated together and also should be coordinated with OC relay that is installed on the Point of Common Coupling (PCC) which is set at Critical Clearing Time (CCT) as a definite time, to have a desirable performance on each outage. In this paper, by the use of graph theory, various branches of a feeder are identified and the constraints for using particle swarm optimization algorithm to optimize the location of protective equipment are derived. In the proposed algorithm, the location, type and direction of relays are optimized simultaneously.

关键词： Microgrid particle swarm optimization algorithm Protection-Coordination Graph theory

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Determining PEMFC Model Parameters with IPSO algorithm

Determining PEMFC Model Parameters with IPSO Algorithm

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2nd International Conference on Energy, Environment and Sustainable Development (EESD 2012)

作者： Wang, Xiaodong Ye, Meiying Xu, Yousheng Zhejiang Normal Univ Dept Elect Engn Jinhua 321004 Peoples R China Zhejiang Normal Univ Dept Phys Jinhua 321004 Peoples R China

ISBN: (纸本)9783037855492

An improved particle swarm optimization algorithm is proposed for determining proton exchange membrane fuel cell (PEMFC) model parameters according to its V-I characteristics. In the algorithm, the weight update is adaptive with the change of objective function. The test results indicate that satisfying parameter accuracy can be achieved by the algorithm. Also, the V-I characteristics obtained by the improved particle swarm optimization algorithm are in good agreement with the simulated data.

关键词： PEMFC Model Parameter Determination particle swarm optimization algorithm

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