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2010 Second International Conference on Communication Systems, Networks and Applications (ICCSNA 2010)

作者： Xiao Zheng1,a,Zhenning Liu1,b School of Economics & Management Jiangxi University of Science and Technology Ganzhou,China

The schedule control of engineering project a unique stage of the project management,which plays an important role in the success or failure of the *** paper analyzed the various impact factors of project progress,and developed the mathematical model of schedule control based on the quantitative description of the relationship between impact factors with the schedule control of the *** order to improve the speed and accuracy of sloving,this article uses the particle swarm algorithm to solve the above ***,the empirical research showed that the method is effective in the field of project schedule control.

关键词： particle swarm algorithm project schedule control

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New Radar Pulse Compression Codes By particle swarm algorithm

New Radar Pulse Compression Codes By Particle Swarm Algorith...

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IEEE Aerospace Conference

作者： Alaa El-Din Sayed Hafez Mohamed Abd El-latif Alexandria University 21D Toson City Alexandria Egpyt Alexandria University 5H Toson City Alexandria Egpyt

ISBN: (纸本)9781457705564

In this paper, new optimized multilevel biphase pulse compression codes (MBPCC) with low peak side lobe (PSL) and mean side lobes (MSL) have been investigated. Amplitude and phase coding have been used during the optimization process. In previous studies, biphase codes were optimized using genetic algorithm (GA). The robustness of genetic algorithm in solving optimization problems degrades simultaneously as the code length increases. particle swarm optimization (PSO) algorithm has been used with MBPCC to solve this problem. PSO algorithm has the merits of rapid convergence to the global optimum. Complete description of PSO implementation process has been clarified. swarm intelligence boasts a number of advantages due to the use of mobile agents. Set of novel codes with optimum PSL ranging between -27.06 dB and -38.26 dB for codes of length 5 up to 45 has been introduced. Optimization results obtained using both PSO and GA has been compared. Comparative study shows both the optimality and superiority of PSO algorithm over GA for different code lengths. MBPCC have a precision of 10~(3) (-30) dB, which can be feasibly implemented using digital attenuators at element level with optimum performance and hardware implementation facility.

关键词： particle swarm algorithm parametric subharmonic oscillator code length Codes Gallium Optimization Procedure particle swarm optimization phase coding Radar pulses Genetic algorithm (GA) Mobile agents optimum performance biphase optimality Amplitude

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FPGA Realizing of Infrared Image Real-time Edge Detection Based on particle swarm algorithm

FPGA Realizing of Infrared Image Real-time Edge Detection Ba...

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2010 International Conference on Test and Measurement (ICTM2010)

作者： Aiping Jiang Electronics Engineering Institute Heilongjiang University Harbin,Heilongjiang Province,China Haibo Tang Electronics Engineering Institute Heilongjiang University Harbin,Heilongjiang Province,China

Infrared imaging technology,especially the infrared guidance,alarm and reconnaissance,has been widely applied in the military ***,the study of infrared image processing technology has practical application value and *** paper uses particle swarm algorithm and FPGA as the core processor to realize the real-time edge detection of infrared *** design uses DE2 of Altera Company as the development platform and Verilog HDL to complete the design of particle swarm algorithm and the whole *** acquisition device adopts TRDB5M development kit that Terasic Company *** image collected is given relevant processing by FPGA,and then sent to VGA for *** whole hardware realization of image processing module greatly improves the processing speed of system,and has the high realtime performance.

关键词： particle swarm algorithm Edge detection FPGA

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Pulse shape analysis for γ-ray tracking (Part II): Fully informed particle swarm algorithm applied to AGATA

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EUROPEAN PHYSICAL JOURNAL A 2011年第10期47卷 131-131页

作者： Schlarb, M. Gernhaeuser, R. Klupp, S. Kruecken, R. Tech Univ Munich Phys Dept E12 D-85748 Garching Germany TRIUMF Vancouver BC V6T 2A3 Canada

AGATA is an array of highly segmented HPGe detectors using the technique of gamma-ray tracking to reconstruct the scattering path of gamma-rays interacting within the active material. A basic requirement therefore is a precise reconstruction of the individual interaction locations within the detectors. This is possible through the use of pulse shape analysis (PSA) which, however, has to be conducted in real time due to the high data rates expected. We report on the use of a fully informed particle swarm (FIPS) algorithm for PSA. The achieved position resolution, in the order of a few millimeters, is within the requirements of a.-ray tracking array. The energy resolutions in Doppler-corrected spectra of a simulated in-beam experiment are comparable to those of a non-real-time extensive PSA based on a full grid search.

关键词： tracking arrays Energy resolution particle swarm algorithm ray tracking shape analysis Pulse shape high purity Ge detectors Real time Data transfer rates

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Distributed precoder with a novel version of particle swarm optimisation algorithm in QR decomposition-based multi-relay systems

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IET COMMUNICATIONS 2010年第13期4卷 1607-1616页

作者： Long, H. Zheng, K. Wang, F. Wei, M. Wang, W. Beijing Univ Posts & Telecommun Wireless Signal Proc & Network Lab Key Lab Universal Wireless Commun Minist Educ Beijing 100876 Peoples R China

The QR decomposition (QRD)-based relaying protocol can achieve both the distributed array gain and the intra-node array gain in multi-input multi-output multi-relay systems. In this paper, the distributed precoding technique is first utilised in the QRD-based multi-relay system to further improve the capacity performance of the system. In order to find the optimum distributed precoder, a novel version of the particle swarm optimisation (PSO) algorithm is presented by redefining the evolution of particles on the spherical surface. Then, the complexity analysis of the proposed PSO algorithm is carried out to show its feasibility. Finally, numerical and simulation results demonstrate the performance gain of the proposed design in terms of the ergodic capacity.

关键词： quick release particle swarm optimization Distribute Relay systems Gain particle swarm algorithm parametric subharmonic oscillator

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Enhanced adaptive particle swarm optimisation algorithm for dynamic economic dispatch of units considering valve-point effects and ramp rates

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IET GENERATION TRANSMISSION & DISTRIBUTION 2012年第5期6卷 424-435页

作者： Niknam, T. Golestaneh, F. Shiraz Univ Technol Dept Elect & Elect Engn Shiraz Iran

In power systems, dynamic economic dispatch (DED) is one of the most significant non-linear problems showing non-convex characteristic because of the valve-point effects. In this study, an enhanced adaptive particle swarm optimisation (EAPSO) algorithm is proposed to solve the DED problem where the valve-point effects, ramp-rate limits and transmission power losses are taken into account. In the proposed optimisation algorithm, a mutation technique is devised to prevent premature phenomena and lead the swarm search space much more effectively;also a novel non-linear approach is designed to adjust the inertia weight factor dynamically according to the optimisation process performance. Social and cognitive factors are self-adaptively tuned, so the swarm can search the space smartly for global optimum solution. The efficiency of the proposed method is validated on three popular test systems in the area including 5, 10 and 30 thermal units. The results are compared with most of the other works in the area. The superiority of the method is shown over earlier methods.

关键词： Test systems HEAT RATE valve effect Work area particle swarm optimization Optimization algorithms Dispatching dike swarms particle swarm algorithm Self tuning

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The algorithm Research of Adaptive Noise Cancellation Based on Dual Arrays and particle swarm algorithm

The Algorithm Research of Adaptive Noise Cancellation Based ...

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2011 International Conference on Fuzzy Systems and Neural Computing(FSNC 2011)

作者： Tan Jingjing Zhao Ping Wang Wei Wei Xu Wei Yong School of Electronics and Information Northwestern Polytechnical University

In the complex application environment of fuzzy sound tracking,the source direction,amplitude and phase fuzzy problems are inevitably existed in signal processing from microphone arrays,so a kind of adaptive noise cancellation algorithm based on array particle swarm algorithm is proposed in this *** signal is collected through the beam-forming technology,particle swarm algorithm is applied in adaptive noise cancellation to solve the fuzzy *** simulation results show that this algorithm has better denoising ability and performance than the traditional method,SNR is also greatly improved,and the fuzzy problems of speech data can be solved effectively.

关键词： particle swarm algorithm array beamforming fuzzy information processing adaptive noise canceller

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Improved particle swarm algorithm and its application in Vehicle Routing Problem

Improved Particle Swarm algorithm and its application in Veh...

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International Conference on Broadcast Technology and Multimedia Communication(BTMC 2010)

作者： Ding Pengxin School of Computer Science Chengdu University of Information Technology Chengdu, Sichuan 610225, China Zou Shurong and Zhang Hongwei School of Computer Science Chengdu University of Information Technology Chengdu, Sichuan 610225, China

In this paper, the particle swarm algorithm is combined with the mutation operation method, according to the actual problem constraints, the particle swarm intelligence factor was added. Classify the location of each customer according to the coordinates, sort the service time of each customer, and then, and then map the results of classification and ordination into particle, which is involved in the operation of the particle swarm algorithm. The experimental results showed that this method is effective.

关键词： vehicle routing particle swarm algorithm multiple objectives intelligence factor

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Enhanced intelligent proportional-integral-like fuzzy knowledge-based controller using chaos-enhanced accelerated particle swarm optimization algorithm for transient calibration of air-fuel ratio control system

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING 2020年第1期234卷 39-55页

作者： Li, Ziyang Zhou, Quan Zhang, Yunfan Li, Ji Xu, Hongming Univ Birmingham Sch Engn Birmingham B15 2TT W Midlands England

The self-adaptive and highly robust proportional-integral-like fuzzy knowledge-based controller has been developed to regulate air-fuel ratio for gasoline direct injection engines, in order to improve the transient response behaviour and reduce the effort to be spent on calibration of parameter settings. However, even though the proportional-integral-like fuzzy knowledge-based controller can automatically correct the initially calibrated proportional and integral parameters, a more appropriate selection of controller parameter settings will lead to better transient performance. Thus, this article proposes an enhanced intelligent proportional-integral-like fuzzy knowledge-based controller using chaos-enhanced accelerated particle swarm optimization algorithm to automatically define the most optimal parameter settings. An alternative time-domain objective function is applied for the transient calibration programme without the need for prior selection of the search-domain. The real-time transient performance of the enhanced controller is investigated on the air-fuel ratio control system of a gasoline direct injection engine. The experimental results show that the enhanced proportional-integral-like fuzzy knowledge-based controller based on chaos-enhanced accelerated particle swarm optimization is able to damp out the oscillations with less settling time (up to 75% reduction) and less integral of absolute error (up to 64.07% reduction) compared with the conventional self-adaptive proportional-integral-like fuzzy knowledge-based controller. Repeatability tests indicate that the chaos-enhanced accelerated particle swarm optimization algorithm-based proportional-integral-like fuzzy knowledge-based controller is also able to reduce the mean value of objective function by up to 10.61% reduction and the standard deviation of the objective function by up to 28.29% reduction, compared with the conventional accelerated particle swarm optimization algorithm-based proportional-int

关键词： Gasoline direct injection engine fuzzy control air-fuel ratio multiple-objective optimization particle swarm algorithm transient calibration

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Radial basis function network-based transform for a nonlinear support vector machine as optimized by a particle swarm optimization algorithm with application to QSAR studies

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JOURNAL OF CHEMICAL INFORMATION AND MODELING 2007年第4期47卷 1438-1445页

作者： Tang, Li-Juan Zhou, Yan-Ping Jiang, Jian-Hui Zou, Hong-Yan Wu, Hai-Long Shen, Guo-Li Yu, Ru-Qin Hunan Univ State Key Lab Chemo Biosensing & Chemometr Coll Chem & Chem Engn Changsha 410082 Peoples R China

The support vector machine (SVM) has been receiving increasing interest in an area of QSAR study for its ability in function approximation and remarkable generalization performance. However, selection of support vectors and intensive optimization of kernel width of a nonlinear SVM are inclined to get trapped into local optima, leading to an increased risk of underfitting or overfitting. To overcome these problems, a new nonlinear SVM algorithm is proposed using adaptive kernel transform based on a radial basis function network (RBFN) as optimized by particle swarm optimization (PSO). The new algorithm incorporates a nonlinear transform of the original variables to feature space via a RBFN with one input and one hidden layer. Such a transform intrinsically yields a kernel transform of the original variables. A synergetic optimization of all parameters including kernel centers and kernel widths as well as SVM model coefficients using PSO enables the determination of a flexible kernel transform according to the performance of the total model. The implementation of PSO demonstrates a relatively high efficiency in convergence to a desired optimum. Applications of the proposed algorithm to QSAR studies of binding affinity of HIV-1 reverse transcriptase inhibitors and activity of 1-phenylbenzimidazoles reveal that the new algorithm provides superior performance to the backpropagation neural network and a conventional nonlinear SVM, indicating that this algorithm holds great promise in nonlinear SVM learning.

关键词： Support Vector Network Radial basis function networks reverse transcriptase, Human immunodeficiency virus 1 particle swarm optimization Quantitative Structure-Activity Relationship Transform nonlinearity particle swarm algorithm parametric subharmonic oscillator

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