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

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PSO algorithm for Block Sequencing Problem in Open Pit Mining

PSO Algorithm for Block Sequencing Problem in Open Pit Minin...

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2015 5th International Conference on Computer Sciences and Automation Engineering(ICCSAE 2015)

作者： Wenbo Liu Hongyi Zhang Liaoning Key Laboratory of Manufacturing System and Logistics The Logistics InstituteNortheastern University

Iron ore is the main raw material of the production in iron and steel enterprises in China. It is a non-renewable resource and with limited reserves. In this paper, in combination with the practical situation of the mine, an integer linear programming model of a strip mining block scheduling problem is established with the scientific, reasonable, and economical principle. The maximization of mining profit is set as the goal under the precedence constraints and production capacity constraints. This paper designs an improved particle swarm optimization algorithm to solve the problem and compares with results with CPLEX optimizing software. According to the results of different scale experiment, the improved particle swarm optimization algorithm has better performance.

关键词： Mining particle swarm optimization algorithm CPLEX

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基于光纤光谱技术的羊肉掺假检测

基于光纤光谱技术的羊肉掺假检测

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作者：成甜甜河北农业大学

学位级别：硕士

Lamb meat is delicious and rich in nutritional *** is deeply loved by consumers and its demand at home and abroad continues to ***,because many mutton processing companies are difficult to manage,it provides opportunities for some illegal vendors,which has led to the intensification of mutton *** order to protect the rights and health of consumers,meat adulteration testing is *** present,some chemical methods have been used to detect whether meat is adulterated,but these methods have certain defects,which damage the meat and cause a certain waste of time or *** order to make up for the lack of chemical methods,the paper uses fiber optic spectroscopy to adulterate ***-depth research on detection *** main work of this paper is as follows:(1)Pretreatment of spectral ***,the spectral reflectance of the adulterated mutton samples was obtained based on the optical fiber spectroscopy ***,the original spectral data was preprocessed,and the Savitzky-Golay convolution smoothing method was adopted to reduce the noise of spectral data,smooth the spectral curve,and improve the signal to noise ratio of *** data with different dimensions are normalized to the interval[0,1]to make the data *** data were provided for the establishment of mutton adulteration detection model.(2)A qualitative detection model of mutton adulteration based on weighted random forest algorithm was *** sample can be divided into two categories:adulteration pure samples of mutton and mutton samples,adulterated mutton samples including "lamb-chicken" and "lamb-pork" adulterated samples,established the mutton adulterated detection model based on random forest algorithm,identify adulteration sample is pure lamb or mutton,results show that the model accuracy is 96.67%.Then further to identify mutton adding meat,sample can be divided into three categories:pure samples of mutton,"lamb-chicken" adulterated sample

关键词： Detection of mutton adulteration Random forest Least squares support vector machines particle swarm optimization algorithm

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Exploratory Developemnt of Parameters Identify Based on Reformative PSO algorithm in DTC System

Exploratory Developemnt of Parameters Identify Based on Refo...

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第一届国际测量仪器与测试自动化学术会议

作者： Cao Chengzhi Wu YuSheng (Shenyang University of Technology. XingHua Street 110023 China)

＜正＞In order to improve the stability and dependability of the Direct Torque Control （DTC）system in low speed state of asynchronous dynamo.a kind of reformative particle swarm optimization（PSO）algorithm. which optimizes the Wavelet Neural Network（WNN）,is used for observing parameters which contain rev. magnetic likage and stator resistance, insteading of conventional velocity generator and magnetic likage sight. Relativing to the problems, such as easily sinking into the part optimal *** speed in astringency. short exactitude in precision and so on. this reformative method divides the optimize particles into two teams, one of them adopts part particle swarm algorithm which adhibits compressibility factor, the others adopts global particle swarm algorithm which adhibits inertial weighting, both the local value and the global value can be compromised for improving the astringency speed and precision through combining these two teams. At the same time the network can constitute the link between wavelet transform and network quotiety. which is used for observing parameters. Passed by the validate of experimental result, this kind of reformative PSO algorithm which optimal WNN can fast converge, and it has good exactitude in precision. So it can make the asynchronous dynamo keep high capability, especially in low speed state.

关键词： Direct torque control particle swarm optimization algorithm wavelet neural network part optimal value.

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Application research of improved K-means algorithm in network intrusion detection

Application research of improved K-means algorithm in networ...

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2017 International Conference on Education and Management Innovation(EMI2017)

作者： Gongrang Zhang Wei Hu School of Management Hefei University of Technology Anhui Transport Interconnection Management Center School of Computer & InformationHefei University of Technology

The original K-means algorithm is sensitive to the selection of the initial clustering center and unstable in the network intrusion detection application based on data mining. In this paper, the optimization ability of particle swarm optimization(PSO) was used to solve the problem that K-means algorithm is sensitive to the selection of the initial clustering center. Different global optimal solutions were obtained by PSO algorithm and used as the basis of choosing the initial clustering center of K-means clustering algorithm. Based on this idea, the K-means algorithm was improved and a network intrusion detection model was established. Experimental results show that the improved K-means clustering algorithm based on PSO has better clustering effect than original K-means clustering algorithm, and can detect more intrusion behaviors in network intrusion detection.

关键词： Network intrusion detection K-means clustering algorithm particle swarm optimization algorithm Initial clustering center

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Research on the Influence of Aerobic Exercise on Athletes' Body Shape and Function Based on Multiple Linear Regression

Research on the Influence of Aerobic Exercise on Athletes' B...

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作者： Xiaojing Zhou Shanghai Lixin Accounting Institute of Finance

Cardiopulmonary Function Test of Athletes is the Key to Scientifically and Reasonably Formulate Training Plans. in Order to Solve the Problem of Large Errors in the Existing Cardiorespiratory Function Detection Methods, a Multiple Linear Regression Cardiorespiratory Function Detection Method Based on particle swarm optimization is Proposed. through Significant Difference Correlation Evaluation, the Metabolic Circulation Function in Sports is Analyzed to Realize Comprehensive Evaluation of Athletes' Absolute Strength, Speed Strength and Strength Endurance, and the Internal Relationship between Athletes' Aerobic Metabolism Ability and Anaerobic Metabolism Ability is Obtained. the Results Show That 3 Months Aerobic Exercise Can Obviously Improve the Body Shape and Physiological Function of Young Women. particle swarm optimization is Used to Optimize and Improve the Speed and Accuracy of Cardiopulmonary Function Detection. the Method Can Effectively Improve the Cardiopulmonary Function of Athletes Before and after Aerobic Training, and Has High Modeling Accuracy.

关键词： Multiple Linear Regression particle swarm optimization algorithm Aerobic Exercise Body Shape Function

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Fault Diagnosis Approach for Pneumatic Control Valves Based on Modified Expert System

Fault Diagnosis Approach for Pneumatic Control Valves Based ...

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第十九届分布式计算及其应用国际学术研讨会

作者： Daoming Zhang Jixing Hao Liming Chen Linna Zhou School of Information and Control Engineering China University of Mining and Technology

This paper studies the fault diagnosis method of pneumatic control valve. Firstly, the faults characteristics of pneumatic control valves are analyzed according to the operating principle and status of pneumatic control valves;secondly, the expert experience of the fault diagnosis of pneumatic control valves is summarized, which is verified according to the operating mechanism;thirdly, a fault diagnosis approach for pneumatic control valves based on modified expert system is proposed, by combining particle swarm optimization(PSO) algorithm with expert rules. Finally, the availability and advantages of the proposed approach is verified by the designed valve experimental system platform. The results show that compared with the basic expert system-based method, the modified method improves the accuracy and reduces the false negative rate effectively.

关键词： pneumatic control velve fault diagnosis expert system particle swarm optimization algorithm

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The State of Charge Estimation of Lithium-ion Batteries Using an Improved Extreme Learning Machine Approach

The State of Charge Estimation of Lithium-ion Batteries Usin...

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第34届中国控制与决策会议

作者： Wei He Hongyan Ma Yingda Zhang Shuai Wang Jiaming Dou Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and Architecture National Virtual Simulation Experimental Center for Smart City Education

Accurate state of charge(SOC) estimation is of great significance for a lithium-ion battery to ensure its safe operation and prevent it from over-charging or ***,it is difficult to get an accurate value of SOC since it is an inner state of a battery cell,which cannot be directly *** order to improve the estimation accuracy of SOC,this paper develops a SOC estimation model for a lithium-ion battery using a particle swarm optimization-Extreme Learning Machine(PSO-ELM) *** PSO is applied to determine the optimal value of hidden layer neurons and the learning rate since these parameters are the most critical factors in constructing an optimal ELM *** inputs to the PSO-ELM model are the battery voltage,current,and temperature,and the output is the actual SOC *** performance of the proposed model is compared with BP neural network and ELM models and verified based on the mean square error(MSE),mean absolute error(MAE),mean absolute percentage error(MAPE),and SOC *** results demonstrate that the PSO-ELM model offers higher accuracy and lower SOC error rate than ELM and BP neural network models.

关键词： Lithium-ion Battery State of Charge particle swarm optimization algorithm Extreme Learning Machine

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Dynamic Scheduling of Multi-task for Hybrid Flow-shop Based on Idle Time Windows

Dynamic Scheduling of Multi-task for Hybrid Flow-shop Based ...

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2009中国控制与决策会议

作者： Zeng Ling-li,Zou Feng-xing,Gao Zheng,Xu Xiao-hong Department of Automatic Control,College of Mechatronics and Automation,National University of Defense Technology, 410073,Changsha,P.R.China

This paper presents an approach based on idle time windows(ITWs) and particle swarm optimization(PSO) algorithm to solve dynamic scheduling of multi-task for hybrid *** idea of ITW is introduced,then the dynamic updating rules of the sets of ITWs are explained in *** the sets of ITWs of machines as constraints, the mathematical model is presented for dynamic scheduling of multi-task for hybrid *** PSO algorithm is proposed in order to solve this *** results of simulation indicate that this approach satisfies the demand of dynamic scheduling of multi-task.

关键词： Idle Time Window Hybrid Flow-shop Multi-task Dynamic Scheduling particle swarm optimization algorithm

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An Improved PSO-BP Neural Network and Its Application to Earthquake Prediction

An Improved PSO-BP Neural Network and Its Application to Ear...

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第28届中国控制与决策会议

作者： Cao Li Xiaoyu Liu School of Information Science and Engineering Wuhan University of Science and Technology

ISBN: (纸本)9781467397155

This paper presents a way of combining BP(Back Propagation) neural network and an improved PSO(particle swarm optimization) algorithm to predict the earthquake *** is known that the BP neural network and the normal PSO-BP neural network have some defeats,such as the slow convergence rate,easily falling into local minimum *** improving the properties of PSO,some proposed the linear decreasing inertia weight ***,this paper uses a nonlinear decreasing inertia weight in PSO to get a faster training speed and better optimal *** with the linear decreasing strategy,the inertia weight in our nonlinear method has a faster declining speed in the early iteration,which can enhance the searching *** the late iteration,the inertia weight has a slower declining speed to avoid trapping in local minimum *** we apply the improved PSO to optimize the parameters of BP neural *** the end,the improved PSO-BP neural network is applied to earthquake *** simulation results show that the proposed improved PSO-BP neural network has faster convergence rate and better predictive effect than the BP neural network and the normal PSO-BP neural network.

关键词： BP neural network particle swarm optimization algorithm Inertia Weight Earthquake Prediction

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Multi-performance optimization of Cement Blending Process

Multi-performance Optimization of Cement Blending Process

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第三十届中国控制会议

作者： LI Junli,MAO Jianlin,ZHANG Guanghui College of Information Engineering and Automation,Kunming University of Science and Technology,Kunming 650051,P.R.China

In order to effectively solve the blending optimization problem in cement production process,an optimization model integrating production indices,the cost and its solution method are ***,the cost and the rate values of raw materials are incorporated into the optimization model respectively as the objective *** function is used to transform optimization problem with constraints into an unconstrained *** then particle swarm optimization algorithm(PSO) is applied to search the optimum *** the optimization information of swarm becomes stagnant,the "inertia weight" operator,cross and mutation operations based on protection strategy are introduced in the optimization process that make the algorithm to maintain diversity and better convergence *** calculation result shows that the presented method can effectively improve the global search ability and convergence *** optimization result can improve the passing rate of indicators by reducing production cost and the harmful ingredient of the mixed material.

关键词： Blending optimization Penalty function particle swarm optimization algorithm Inertia weight Protection strategy

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