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检索条件"主题词=Particle swarm optimization algorithm"

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Network Intrusion Detection Analysis with Neural Network and particle swarm optimization algorithm

Network Intrusion Detection Analysis with Neural Network and...

引用

The 22nd China Control and Decision Conference(2010年中国控制与决策会议)

作者： WenJie Tian JiCheng Liu Beijing Automation Institute of Beijing Union University Beijing China 100101

Because the network intrusion behaviors are characterized with uncertainty, complexity and diversity, an intrusion detection method based on neural network and particle swarm optimization algorithm (PSOA) is presented in this paper. The novel structure model has higher accuracy and faster convergence speed. We construct the network structure, and give the algorithm flow. We discussed and analyzed the impact factor of intrusion behaviors. With the ability of strong self-learning and faster convergence, this intrusion detection method can detect various intrusion behaviors rapidly and effectively by learning the typical intrusion characteristic information. Utilizing the character that rough set can keep the discern ability of original dataset after reduction, the reduces of the original dataset are calculated and used to train neural network, which increase the detection accuracy. We apply this technique on KDD99 data set and get satisfactory results. The experimental result shows that this intrusion detection method is feasible and effective.

关键词： Neural Network Rough Set Network Intrusion Reduction particle swarm optimization algorithm

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Fault Diagnosis Analysis with Support Vector Regression and particle swarm optimization algorithm

Fault Diagnosis Analysis with Support Vector Regression and ...

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The 22nd China Control and Decision Conference(2010年中国控制与决策会议)

作者： WenJie Tian JiCheng Liu Beijing Automation Institute of Beijing Union University Beijing China 100101

The fault diagnosis model with support vector regression (SVR) and particle swarm optimization algorithm (POSA) for is proposed. The novel structure model has higher accuracy and faster convergence speed. We construct the network structure, and give the algorithm flow. The impact factor of fault behaviors is discussed. With the ability of strong self-learning and faster convergence, this fault detection method can detect various fault behaviors rapidly and effectively by learning the typical fault characteristic information. Utilizing the character that principal components analysis algorithm can keep the discern ability of original dataset after reduction, the reduces of the original dataset are calculated and used to train individual SVR for ensemble, and consequently, increase the detection accuracy. To validate the effectiveness of the proposed method, simulation experiments are performed based on the electronic circuit dataset. The results show that the proposed method is a promised method owning to its high diversity, high detection accuracy and faster speed in fault diagnosis.

关键词： Support Vector Regression Principal Components Analysis Fault Diagnosis Reduction particle swarm optimization algorithm

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High-precision clock synchronization based on PSO algorithm under the small sample condition

High-precision clock synchronization based on PSO algorithm ...

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2016 IEEE Advanced Information Management,Communicates,Electronic and Automation Control Conference(IMCEC 2016)

作者： Yilin Jiang Fangyuan Zhang Muran Guo College of Information and Communication Engineering Harbin Engineering University

ISBN: (纸本)9781509001668

In order to solve the problem of clock synchronization under the condition of small sample set,a state optimal estimation method of data fusion bases on natural selection PSO algorithm is ***,use Kalman filtering algorithm and Bootstrap method to local filtering process the measurement data under the condition of Monte Carlo simulation experiment,and the reliability of the data is ***,use natural selection PSO algorithm to fuse the data,the optimal fused estimation is achieved and the fusion accuracy is ***,the fusion result is considered as the new information of virtual master clock,which could exchange time messages with node clock of network from the ***,clock synchronization in the network is *** simulated results show that the accuracy is *** precision clock synchronization is realized and the stability time is decreased.

关键词： clock synchronization data fusion particle swarm optimization algorithm Bootstrap method Kalman filtering

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Adaptive Weight particle swarm optimization algorithm with Constriction Factor

Adaptive Weight Particle Swarm Optimization Algorithm with C...

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2010 International Conference of Informationa Science and Management Engineering(2010年信息科学与管理工程国际学术会议 ISME 2010)

作者： Zhiyu You Weirong Chen Guojun He Xiaoqiang Nan School of Electrical Engineering Southwest Jiaotong University Chengdu China

In order to overcome the shortage of premature convergence caused by local optimization in the process of global optimization, an adaptive weight particle swarm optimization algorithm with constriction factor is proposed combined with an analysis of convergence of particle swarm optimization algorithm. The value of the inertia weight is set according to dynamic information about the changes in the objective function value, as to effectively balance the advantages of global optimization against the shortage of local optimization. Four Benchmark function are used for performance test of five different kinds of optimization algorithm, the final results shows that the proposed method has a good ability to slow down the pace of premature convergence, compared to other improved particle swarm algorithm.

关键词： particle swarm optimization algorithm convergence adaptive weight constriction factor

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Short Term Wind Speed Forecasting Using Wavelet Transform and Grey Model Improved by particle swarm optimization 5

Short Term Wind Speed Forecasting Using Wavelet Transform an...

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5th International Conference on Electric Utility Deregulation and Restructuring and Power Technologies (DRPT)

作者： Guo, Mian Wei, Zhinong Zang, Haixiang Sun, Guoqiang Li, Huijie Cheung, Kwok W. Hohai Univ Coll Energy & Elect Engn Nanjing Jiangsu Peoples R China Alston Power Grid Technol Ctr Co Ltd Shanghai Peoples R China GE Grid Solut Inc Redmond WA USA

ISBN: (纸本)9781467371063

Nowadays wind energy is one of the most important source of renewable energy worldwide. Wind power generation is an important form of wind energy utilization. The energy problem has become increasingly prominent, which requires to speeding up the development of wind energy industry. However, the existing wind speed forecasting using grey model is inaccurate. Direct prediction of original wind speed sequence produces large error because of the randomness of wind power. To solve the above problems, a novel method for short term wind speed forecasting based on grey model is proposed in this paper. In order to reduce the error of short term wind speed forecasting, one of the most successful approaches is particle swarm optimization algorithm, which chooses the parameters of grey model to avoid the man-made blindness and enhances the efficiency and capability of forecasting. In the present paper, the wavelet decomposition and reconstruction are used to separate the high frequency signal and the low frequency signal. To verify its efficiency, this proposed method is applied to a wind farm's wind speed forecasting in China. The result confirms that the performance of the method proposed in this paper is much more favorable in comparison with the original methods studied.

关键词： wind speed forecasting wind power generation grey model particle swarm optimization algorithm wavelet decomposition

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Quantitative analysis of multi-component complex oil spills based on the Least-Squares Support Vector Regression

Quantitative analysis of multi-component complex oil spills ...

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International Symposium on Hyperspectral Remote Sensing Applications / International Symposium on Environmental Monitoring and Safety Testing Technology

作者： Tan Ailing Zhao Yong Wang Siyuan Yanshan Univ Sch Informat Sci & Engn Key Lab Special Fiber & Fiber Sensor Hebei Prov Qinhuangdao 066004 Peoples R China Yanshan Univ Inst Elect Engn Qinhuangdao 066004 Peoples R China

ISBN: (数字)9781510607712

ISBN: (纸本)9781510607705;9781510607712

Quantitative analysis of the simulated complex oil spills was researched based on PSO-LS-SVR method. Forty simulated mixture oil spills samples were made with different concentration proportions of gasoline, diesel and kerosene oil, and their near infrared spectra were collected. The parameters of least squares support vector machine were optimized by particle swarm optimization algorithm. The optimal concentration quantitative models of three-component oil spills were established. The best regularization parameter C and kernel parameter sigma of gasoline, diesel and kerosene model were 48.1418 and 0.1067;53.2820 and 0.1095;59.1689 and 0.1000 respectively;The decision coefficient R-2 of the prediction model were 0.9983. 0.9907 and 0.9942 respectively;RMSEP values were 0.0753, 0.1539 and 0.0789 respectively;For gasoline, diesel fuel and kerosene oil models, the mean value and variance value of predict absolute error were -0.0176 +/- 0.0636 mu L/mL, -0.0084 +/- 0.1941 mu L/mL, and 0.00338 +/- 0.0726 mu L/mL respectively. The results showed that each component's concentration of the oil spills samples could be detected by the NIR technology combined with PSO-LS-SVR regression method, the predict results were accurate and reliable, thus this method can provide effective means for the quantitative detection and analysis of complex marine oil spills.

关键词： Near Infrared Spectroscopy Complex oil spills Least squares support vector regression particle swarm optimization algorithm

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Power System Oscillation Damping by Intelligent Power System Stabilizer 1

Power System Oscillation Damping by Intelligent Power System...

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1st IEEE International Conference on Power Electronics, Intelligent Control, and Energy Systems (ICPEICES)

作者： Bhattacharya, Sragdhara Natl Inst Technol Patna Dept Elect Engn Patna Bihar India

ISBN: (纸本)9781467385879

Power Systems are inherently non-linear systems that are frequently subjected to various disturbances causing oscillations at low frequencies that may lead to instability. Generators are usually provided with power system stabilizers minimize the effect of these oscillations. The objective of this paper is find the optimal parameters for a conventional lead-lag compensator based Power System Stabilizer (PSS) for a system comprising of a generator connected to an infinite bus and containing a STIA type excitation system. The tuning of the parameters of the Power System Stabilizer is accomplished using the particle swarm optimization (PSO) algorithm. In this paper, a Fuzzy Power System Stabilizer (FPSS) where the optimal values of the parameters of the FPSS are decided using the PSO algorithm is also designed. The particle swarm optimization based conventional PSS and the particle swarm optimization based Fuzzy PSS are also incorporated in a system containing multiple machines to check the system responses under different loading conditions and faults of different types. The simulation results clearly prove the efficiency of the PSO based conventional and fuzzy power system stabilizers in damping the low frequency speed and power oscillations occurring in the power system due to various disturbances.

关键词： Fuzzy Logic based Power System Stabilizer Lead-lag Power System Stabilizer Multi-machine system Oscillation damping particle swarm optimization algorithm

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Small-Signal Stability Enhancement of Multimachine Power System using Bio-inspired algorithms

Small-Signal Stability Enhancement of Multimachine Power Sys...

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National Power Systems Conference (NPSC)

作者： Chitara, Dhanraj Niazi, K. R. Swarnkar, Anil Gupta, Nikhil Malaviya Natl Inst Technol Dept Elect Engn Jaipur Rajasthan India

ISBN: (纸本)9781467399685

In this paper small signal stability improvement of two-area, four-machine Kundur power system stabilizer is presented using Cuckoo Search algorithm (CSA), particle swarm optimization algorithm (PSOA) and Genetic algorithm (GA). For this the propose, the problem is formulated using an eigenvalue based multiobjective function that shift unstable or poorly damped modes to specific D-shape region in the left-half of the s-plane by controlling the damping ratio and damping factor. To show the effectiveness and superiority of the proposed CSA based PSS (CSAPSS), the non-linear time domain simulations are compared with GA based PSS (GAPSS), PSO based PSS (PSOAPSS) for different line outages and different scenarios of severe disturbances. The robustness of proposed CSAPSS is evaluated by performances indices for wide range of loading conditions with severe faults. Moreover, the lower value of performances indices for proposed CSAPSS than to GAPSS, PSOAPSS exhibit its relative stability.

关键词： cuckoo search algorithm genetic algorithm particle swarm optimization algorithm power system stabilizer small signal stability

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Deceptive Problems of particle swarm optimization algorithm and Improvement of Their Performance

Deceptive Problems of Particle Swarm Optimization Algorithm ...

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2010 International Colloquium on Computing,Communication, Control, and Management (CCCM2010)

作者： Shu Wang~1 1Department of Computer Science Yangzhou University Yangzhou,China Ling Chen~(1,2) 2State Key Lab of Novel Software Technology Nanjing University Nanjing,China

This paper introduces two instances of deceptive problems of particle swarm optimization(PSO).We theoretically prove that PSO can not converge to the global optimal solution of those two problems under certain *** addition,we empirically verified the correctness of our theoretical *** order to solve the two deceptive problems,we propose an algorithm ISA-PSO (inverse search area-PSO),which can change the particles' search direction and expand their search area at the appropriate *** experimental results show the effectiveness of our algorithm ISA-PSO.

关键词： particle swarm optimization algorithm Deceptive problems Convergence

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Network Intrusion Detection Analysis with Neural Network and particle swarm optimization algorithm

Network Intrusion Detection Analysis with Neural Network and...

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2010 Chinese Control and Decision Conference

作者： WenJie Tian,JiCheng Liu Beijing Automation Institute of Beijing Union University,Beijing,China,100101

Because the network intrusion behaviors are characterized with uncertainty,complexity and diversity,an intrusion detection method based on neural network and particle swarm optimization algorithm(PSOA) is presented in this *** novel structure model has higher accuracy and faster convergence *** construct the network structure,and give the algorithm *** discussed and analyzed the impact factor of intrusion *** the ability of strong self-learning and faster convergence,this intrusion detection method can detect various intrusion behaviors rapidly and effectively by learning the typical intrusion characteristic *** the character that rough set can keep the discern ability of original dataset after reduction,the reduces of the original dataset are calculated and used to train neural network,which increase the detection *** apply this technique on KDD99 data set and get satisfactory *** experimental result shows that this intrusion detection method is feasible and effective.

关键词： Neural Network Rough Set Network Intrusion Reduction particle swarm optimization algorithm

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