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2nd International Conference on Computer and Automation Engineering (ICCAE)

作者： Yu Xiaofang Gao Jvlin Song Guodong Inner Mongol Agr Univ Hohhot 010010 Peoples R China Inner Mongolia Autonomous Reg Seed Control Stn Hohhot 010010 Peoples R China

ISBN: (纸本)9781424455690

Utilization efficiency forecasting of moisture content in maize has a great importance to maize production. RBF neural network is able to universal approximation. PSO-RBF neural network which combines particle swarm optimization (PSO) with RBF neural network is proposed to utilization efficiency forecasting of moisture content in maize. Maize fields of the farms in Henan province are applied to study the utilization efficiency forecasting ability of moisture content in maize by the proposed PSO-RBF neural network method. And BP neural network and normal RBF neural network are applied to compare the PSO-RBF neural network method. By analyzing the experimental results, it is indicated that utilization efficiency forecasting ability of moisture content in maize by PSO-RBF neural network than that by RBF neural network and BP neural network.

关键词： utilization efficiency moisture content RBF neural network particle swarm optimization algorithm

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Integration of particle swarm optimization and Immune Genetic algorithm-Based Dynamic Clustering for Customer Clustering

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INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS 2015年第5期24卷 1550019-1550019页

作者： Kuo, R. J. Lin, S. H. Chen, Zhen-Yao Natl Taiwan Univ Sci & Technol Dept Ind Management Taipei 10607 Taiwan Taiwan Semicond Mfg Co Hsinchu 30078 Taiwan De Lin Inst Technol Dept Business Adm New Taipei City 23654 Taiwan

This study intends to present a dynamic clustering (DC) approach based on particle swarm optimization (PSO) and immune genetic (IG) (DCPIG) algorithm, which is able to cluster the data into adequate clusters through data characteristics with pre-specified numbers of clusters. The proposed DCPIG algorithm is compared with three DC algorithms in the literature using Iris, Wine, Glass and Vowel benchmark data sets. The experiment results show that the DCPIG algorithm can achieve higher stability and accuracy than the other algorithms. In addition, the DCPIG algorithm is also applied to a real-world problem considering the customer clustering for a cyber flower shop. Lastly, we recommend different products and services to customers based on the clustering results.

关键词： Cluster analysis dynamic clustering particle swarm optimization algorithm immune genetic 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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22nd Chinese Control and Decision Conference

作者： Tian, WenJie Liu, JiCheng Beijing Union Univ Beijing Automat Inst Beijing 100101 Peoples R China

ISBN: (纸本)9781424451821

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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Intelligent Maintenance Scheduling System for Maximum Performance of Solar-Energy-Generating System

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SENSORS AND MATERIALS 2017年第11期29卷 1579-1588页

作者： Huang, Chung-Chi Far East Univ Dept Automat & Control Engn 49 Zhonghua Rd Tainan 74448 Taiwan

In this research, an intelligent maintenance scheduling system for gaining the maximum performance of a solar-energy-generating system is proposed. In the system, the particle swarm optimization (PSO) algorithm is used for the intelligent maintenance scheduling of the solar energy -generating system. The maintenance center receives many maintenance prescription requests from all solar-energy-generating stations. For various uncertainties, the optimal solution of maintenance scheduling is considered. To reduce maintenance costs and improve maintenance efficiency, the intelligent scheduling system using the PSO method is constructed. In the maintenance scheduling model, the maintenance time and prework time are set to be the optimal computing parameters, and the lowest cost is set to be the optimal target. By design and implementation, we can determine the advantages of the PSO-based intelligent system. It meets both requirements of real time and integration for intelligent systems. Experimental results show the advantages of the intelligent maintenance scheduling system for the maximum performance of the solar-energy-generating system;that is, such a scheduling system meets both requirements of efficiency and lowest cost for the intelligent maintenance of the solar-energy-generating system.

关键词： intelligent maintenance scheduling system particle swarm optimization algorithm solar energy

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A Parallel Markov Cerebrovascular Segmentation algorithm Based on Statistical Model

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Journal of Computer Science & Technology 2016年第2期31卷 400-416页

作者： Rong-Fei Cao Xing-Ce Wang Zhong-Ke Wu Ming-Quan Zhou Xin-Yu Liu Department of Information Science and Technology Beijing Normal University Beijing 100875 China Engineering Research Center of Virtual Reality and Applications Ministry of Education Beijing 100875 China Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China

For segmenting cerebral blood vessels from the time-of-flight magnetic resonance angiography （TOF-MRA） images accurately, we propose a parallel segmentation algorithm based on statistical model with Markov random field （MRF）. Firstly, we improve traditional non-local means filter with patch-based Fourier transformation to preprocess the TOF-MRA images. In this step, we mainly utilize the sparseness and self-similarity of the MRA brain images sequence. Secondly, we add the MRF information to the finite mixture mode （FMM） to fit the intensity distribution of medical images. We make use of the MRF in image sequence to estimate the proportion of cerebral tissues. Finally, we choose the particle swarm optimization （PSO） algorithm to parallelize the parameter estimation of FMM. A large number of experiments verify the high accuracy and robustness of our approach especially for narrow vessels. The work will offer significant assistance for physicians on the prevention and diagnosis of cerebrovascular diseases.

关键词： cerebrovascular segmentation non-local means filtering Markov random field particle swarm optimization algorithm

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PSO-BASED PID CONTROLLER DESIGN FOR AN ENERGY CONVERSION SYSTEM USING COMPRESSED AIR

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TEHNICKI VJESNIK-TECHNICAL GAZETTE 2017年第3期24卷 671-679页

作者： Atacak, Ismail Kucuk, Bayram Gazi Univ Technol Fac Comp Eng Dept TR-06500 Ankara Turkey Gazi Univ Inst Informat TR-06500 Ankara Turkey

In this study, an optimal control algorithm is proposed to overcome low performance problems arising from the non-linear characteristics of pneumatic motor in compressed air-based energy conversion systems. The effectiveness of the proposed algorithm is tested on an energy conversion system which includes a compressor, a proportional valve, a pneumatic motor (PM), a permanent magnet direct current (PMDC) generator and a control card. The control function of the system is carried out by driving the proportional valve with the control signals which is obtained depending on the PMDC generator output voltage error. In this structure, an optimal proportional-integral-derivative (PID) controller which tunes on-line its own gain parameters by particle swarm optimization (PSO) algorithm according to the operating conditions of the system used. In order to observe the effects of PSO-based PID controller on the system performance, the energy conversion system is also controlled by a discrete time PID controller. The experimental results show that PSO-based PID controller provides more robust control performance than discrete time PID controller under various operating conditions.

关键词： compressed air-based energy conversion optimal PID controller particle swarm optimization algorithm pneumatic motor

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A Fault Diagnosis Method of Power Systems Based on an Improved Adaptive Fuzzy Spiking Neural P Systems and PSO algorithms

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Chinese Journal of Electronics 2016年第2期25卷 320-327页

作者： WANG Jun PENG Hong TU Min Prez-Jimnez J. Mario SHI Peng School of Electrical and Information Engineering Xihua University Neijiang Power Supply Company 5. Department of Computer Science and Artificial Intelligence University of Seville 6. School of Electrical and Electronic Engineeringthe University of Adelaide Sichuan Province Key Laboratory of Power Electronics Energy-saving Technologies and Equipment Center for Radio Administration and Technology Development Xihua University

A new fault diagnosis method based on improved Adaptive fuzzy spiking neural P systems(in short,AFSN P systems) and particle swarm optimization(PSO)algorithm is presented to improve the efficiency and accuracy of diagnosis for power systems in this paper. AFSN P systems are a novel kind of computing models with parallel computing and learning ability. Based on our previous works, this paper focuses on AFSN P systems inference algorithms and learning algorithms and builds the fault diagnosis model using improved AFSN P systems for diagnosing effectively. The process of diagnosis based on AFSN P systems is expressed by matrix successfully to improve the rate of diagnosis eminently. Furthermore, particle swarm optimization algorithm is introduced into the learning algorithm of AFSN P systems, thus the convergence speed of diagnosis has a big progress. An example of 4-node system is given to verify the effectiveness of this method. Compared with the existing methods, this method has faster diagnosis speed, higher accuracy and strong ability to adapt to the grid topology changes.

关键词： Fault diagnosis Power systems Member Computing AFSN P systems particle swarm optimization algorithm

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An improved mathematical model and a hybrid metaheuristic for truck scheduling in cross-dock problems

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COMPUTERS & INDUSTRIAL ENGINEERING 2016年 91卷 197-204页

作者： Keshtzari, M. Naderi, B. Mehdizadeh, E. Texas Tech Univ Dept Ind Engn Lubbock TX 79409 USA Kharazmi Univ Dept Ind Engn Fac Engn Tehran Iran Islamic Azad Univ Qazvin Branch Dept Ind Engn Qazvin Iran

Recently, several papers study the problem of scheduling inbound and outbound trucks in cross-dock systems. There is only one attempt to mathematically model the problem. Unfortunately this model is ineffective due to its size complexity. Moreover, although different metaheuristics are proposed in the literature, they are not tailored-made for this problem. In this paper, a new mixed integer programming model is formulated for the problem. Using commercial optimization solvers, the performance of the proposed model is compared with the available model to solve small instances. To solve larger instances, a particle swarm optimization hybridized with a simulated annealing is proposed. Using Taguchi method, the proposed algorithm is tuned. Then, it is evaluated against two other available metaheuristics (genetic algorithm and electromagnetism-like metaheuristic) in the literature. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Supply chain management Cross-dock Mixed integer programming particle swarm optimization algorithm Metaheuristic

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Intellectual Approaches to Improvement of the Classification Decisions Quality on the Base of the SVM Classifier

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Procedia Computer Science 2017年 103卷 222-230页

作者： L. Demidova I. Klyueva Y. Sokolova N. Stepanov N. Tyart Ryazan State Radio Engineering University 59/1 Gagarina Str. Ryazan 390005 Russia Moscow Technological Institute 38A Leninskiy pr. Moscow 119334 Russia

In this paper the hybrid and modified versions of the PSO algorithm applied to improvement of the search characteristics of the classical PSO algorithm in the development problem of the SVM classifier have been offered and investigated. A herewith two hybrid versions of the PSO algorithm assume the use of the classical “Grid Search” (GS) algorithm and the “Design of Experiment” (DOE) algorithm accordingly, and the modified version of the PSO algorithm realizes the simultaneous search of the kernel function type, the parameters values of the kernel function, and also the regularization parameter value. Besides, the questions of applicability of the k nearest neighbors (kNN) algorithm in the development problem of the SVM classifier have been considered.

关键词： support vector machine particle swarm optimization algorithm grid search algorithm hybrid algorithm classification optimization parameters of the kernel function regularization parameter separating hyperplane support vectors.

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optimization on the Impeller of a Low-specific-speed Centrifugal Pump for Hydraulic Performance Improvement

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Chinese Journal of Mechanical Engineering 2016年第5期29卷 992-1002页

作者： PEI Ji WANG Wenjie YUAN Shouqi ZHANG Jinfeng National Research Center of Pumps and Pumping System Engineering and TechnologyJiangsu University

In order to widen the high-efficiency operating range of a low-specific-speed centrifugal pump, an optimization process for considering efficiencies under 1.0Qd and 1.4Qd is proposed. Three parameters, namely, the blade outlet width b2, blade outlet angle β2, and blade wrap angle φ, are selected as design variables. Impellers are generated using the optimal Latin hypercube sampling method. The pump efficiencies are calculated using the software CFX 14.5 at two operating points selected as objectives. Surrogate models are also constructed to analyze the relationship between the objectives and the design variables. Finally, the particle swarm optimization algorithm is applied to calculate the surrogate model to determine the best combination of the impeller parameters. The results show that the performance curve predicted by numerical simulation has a good agreement with the experimental results. Compared with the efficiencies of the original impeller, the hydraulic efficiencies of the optimized impeller are increased by 4.18% and 0.62% under 1.0Qd and 1.4Qd, respectively. The comparison of inner flow between the original pump and optimized one illustrates the improvement of performance. The optimization process can provide a useful reference on performance improvement of other pumps, even on reduction of pressure fluctuations.

关键词： low-specific-speed centrifugal pump optimization optimal Latin hypercube sampling surrogate model particle swarm optimization algorithm numerical simulation

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