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

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Prediction of Building Energy Consumption Based on PSO - RBF Neural Network

Prediction of Building Energy Consumption Based on PSO - RBF...

引用

IEEE International Conference on System Science and Engineering (ICSSE)

作者： Zhang, Ying Chen, Qijun Tongji Univ Sch Elect & Informat Shanghai 200092 Peoples R China

ISBN: (纸本)9781479943678

At present, building energy conservation is a hot topic in urban construction and energy conservation research. Predicting the trend of energy consumption is very meaningful for a whole building energy management. Compared with the other feed-forward neural networks, RBF network learning faster and the ability of function approximation is stronger, but its performance still need to be improved. We use particle swarm optimization algorithm (PSO) to optimize RBF neural network and use the optimized RBF neural network to predict energy consumption in this article. Used the statistical data of the whole society's monthly electricity consumption published online as a sample, and simulated the forecasting method by MATLAB.

关键词： Energy consumption prediction RBF neural network particle swarm optimization algorithm

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Surface illumination controlling system for surface checking of automatic vehicle by using PSO algorithm 53

Surface illumination controlling system for surface checking...

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SICE Annual Conference (SICE)

作者： Lu, Cheng Xiao, Haitao Ogai, Harutoshi Waseda Univ Grad Sch Informat Prod & Syst Kitakyushu Fukuoka Japan

ISBN: (纸本)9784907764456

In manufacturing process of automobile, people always provide steady illumination during the time when automobile moves on the assembly line. Automobile is moving slowly on the assembly line for surface detection, painting or other operations in the factory, for lower power consumption and giving a satisfaction sufficient to meet a requirement of illumination on the specified surface of automobile, we consider an surface illumination controlling system by establishing a virtual scene for these works. To achieve this target, we provide (i) Illumination Environment Simulation;(ii) Illumination Model;(iii) particle swarm optimization algorithm. The performance of the proposed algorithm shows that we achieved illumination rendering on the surface of automatic vehicle.

关键词： illumination controlling system illumination calculation particle swarm optimization algorithm test simulator

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Research on algorithm optimization of Hidden Units Data Centre of RBF Neural Network

Research on Algorithm Optimization of Hidden Units Data Cent...

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3rd International Conference on Civil Engineering and Building Materials (CEBM 2013)

作者： Zhao, Jing-Ying Guo, Hai Li, Xiao-Niu Dalian Univ Technol Sch Elect & Informat Engn Dalian 116600 Peoples R China Dalian Natl Univ Dept Comp Sci & Engn Dalian 116600 Peoples R China

ISBN: (纸本)9783037859155

Common algorithms of selecting hidden unit data center in RBF neural networks were first discussed in this essay, i.e. k-means algorithm, subtractive clustering algorithm and orthogonal least squares. Meanwhile, a hybrid algorithm mixed of k-means algorithm and particle swarm optimization algorithm was put forward. The algorithm used the position of the particles in particle swarm optimization algorithm to help deal with the defects of local clusters resulted from k-means algorithm and to make optimization with the optimal fitness of k-means particle swarm with the aim to make the final optimal fitness better satisfy the requirements.

关键词： subtractive clustering method k-means orthogonal least squares particle swarm optimization algorithm RBF radial basis neural network

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Combining CA and PSO to Solve Flexible Job shop Scheduling Problem

Combining CA and PSO to Solve Flexible Job shop Scheduling P...

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26th Chinese Control and Decision Conference (CCDC)

作者： Zhou Wei Bu Yan-ping Zhou Ye-qing E China Univ Sci & Technol Sch Business Shanghai 200237 Peoples R China

ISBN: (纸本)9781479937066

The flexible job shop scheduling problem (FJSP) is an extension of the classic job shop scheduling problem (JSP), which breaks through the uniqueness of limit resources, allows a procedure in many machines processing and one machine processing many kinds of different types of procedures. It is more practical and complex than JSP. The computational complexity of FjSP is much higher, which disables exact solution methods and makes heuristic approaches more qualified. A hybrid optimization algorithm, CPSO, based on the cultural algorithm and particle swarm optimization algorithm, is proposed in this paper to solve the FJSP. The objective is to minimize makespan. Computational results show that this hybrid method is able to solve efficiently these kinds of problems.

关键词： cultural algorithm particle swarm optimization algorithm flexible job shop scheduling problem makespan

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Research on The Effect of Selection of Objective Function on optimization Results 11

Research on The Effect of Selection of Objective Function on...

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11th World Congress on Intelligent Control and Automation

作者： Xiao, Tiantian Han, Pu Hong, Bo Dong, Ze North China Elect Power Univ Hebei Engn Res Ctr Simulat & Optimized Control Po Dept Automat Baoding 071003 Peoples R China

ISBN: (纸本)9781479958252

In this paper, based on particle swarm optimization (PSO) algorithm to observe the different optimization results by changing the objective function. By comparing indicators of various types of objective function, clearly showing its intuitive respective advantages and disadvantages. Herein we can derived from the comprehensive objective function is a relatively good target function, stability, accuracy and rapidity performance can better meet the requirements of people.

关键词： particle swarm optimization algorithm Objective function Parameter optimization

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Hybrid Heuristic algorithm Based Controller Tuning for a Bioreactor

Hybrid Heuristic Algorithm Based Controller Tuning for a Bio...

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International Conference on Green Computing Communication and Electrical Engineering (ICGCCEE)

作者： Ramya, S. Latha, K. Anna Univ Madras Inst Technol Dept Instrumentat Engn Madras 600025 Tamil Nadu India

ISBN: (纸本)9781479949816

Bioreactor is one of the prime processing units widely employed to produce important chemical and biochemical compounds. In this paper, a hybrid heuristic algorithm has been attempted to tune PID controller for nonlinear Bioreactor model. The hybrid algorithm is a combination of Bacterial Foraging optimization (BFO) and particle swarm optimization (PSO) algorithm. Multiobjective performance indexes such as Integral Square Error, peak overshoot are considered to guide this algorithm for discovering best possible value of controller parameters. The controller tuning procedure is individually discussed for both stable and unstable steady state operating region of simulated bio-reactor model. The effectiveness of the proposed scheme has been validated through a comparative study with BFO, PSO based controller tuning methods proposed in the literature. The results show that, the hybrid method provides improved performance in reference tracking and load disturbance rejection with minimal ISE value.

关键词： Bioreactor Bacterial Foraging Optimisation algorithm particle swarm optimization algorithm PID controller

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Improved particle swarm algorithm with Dynamic Adjustment basing on Velocity Information 10

Improved Particle Swarm Algorithm with Dynamic Adjustment ba...

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10th International Conference on Natural Computation (ICNC)

作者： Chen, Zhuo Liang, Ximing Beijing Univ Civil Engn & Architecture Sch Sci Beijing Peoples R China

ISBN: (纸本)9781479951512

particle swarm optimization algorithm is a simple and effective modern optimization algorithm, but it has the problem of being prone to premature and its convergence rate is slow. A new improved PSO algorithm is hence proposed. In the iteration of the proposed algorithm, the particles are distinguished to be active or stable according to their velocity information. For the active particles, to maintain the diversity of population, they are replaced by selection from its previous generation's position and its reverse point, which combines the strategy of opposition-based learning. While for the stable particles, to enhance the convergence speed and increase the local search capability, they are improved by conjugate gradient method. The proposed improved PSO algorithm has come through numerical experiments of classic test functions. The results showed that, compared with other improved algorithms, this proposed improved PSO algorithm is feasible and effective.

关键词： particle swarm optimization algorithm conjugate gradient algorithm velocity information diversity of population numerical experiment

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Vibration suppression of structure with electromagnetic shunt damping absorber

Vibration suppression of structure with electromagnetic shun...

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16th International Symposium on Applied Electromagnetics and Mechanics (ISEM)

作者： Xie, Shilin Li, Peichao Zhang, Xinong Yan, Bo Xi An Jiao Tong Univ Sch Aerosp State Key Lab Strength & Vibrat Mech Struct Xian 710049 Shaanxi Peoples R China

The electromagnetic shunt damping absorber (EMSDA) is developed based on electromagnetic shunt damping mechanism. The governing equation of planar vibration system equipped with the EMSDA is derived. An optimization method is presented to determine the main working parameters of EMSDA on the basis of the built theoretical model. The objective function minimizing the response variance of system under white noise excitation is formulated. The particle swarm optimization algorithm is employed in optimization. The simulated and experimental studies on vibration control by use of EMSDA are conducted. The results show that the electromagnetic shunt damping absorber can attenuate significantly the structural vibration.

关键词： Electromagnetic shunt damping absorber vibration control optimization particle swarm optimization algorithm

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PSO PARAMETERS optimization FOR EKF AND AKF FOR IM ROTOR SPEED ESTIMATION 16

PSO PARAMETERS OPTIMIZATION FOR EKF AND AKF FOR IM ROTOR SPE...

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16th International Power Electronics and Motion Control Conference and Exposition (PEMC)

作者： El Merraoui, K. Ferdjouni, A. Univ Saad Dahleb Dept Elect F Technol Blida Algeria

ISBN: (纸本)9781479920600

This paper presents the application of a Metaheuristic optimization algorithm for determining the parameters of a PI controller and the values of the state and measurement noise of Kalman Filter. The particle swarm optimization is a new technique that is used to solve complex problems. It minimizes a cost function under the cooperation of many individuals. Kalman Filter is used here to estimate the stator currents and rotor fluxes of the induction motor. The performances of the extended Kalman Filter and the adaptive Kalman Filter are analyzed. They are applied to estimate stator currents;rotor fluxes and rotor speed of the induction motor, and thus help to overcome the speed sensor, which is expensive and bulky. The extended Kalman Filter requires extending the state vector to rotor speed, which implies to use the linearization of the model. The adaptive Kalman Filter consists of determining the rotor speed adaptation law. The stability of the estimation error is proved using a Lyapunov function.

关键词： particle swarm optimization algorithm Kalman Filter induction motor Lyapunov function adaptation law

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Online optimization for the multi-objective operation of electric multiple units based speed limits curve 11

Online optimization for the multi-objective operation of ele...

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11th World Congress on Intelligent Control and Automation

作者： Yang, Hui Liu, Hong-En Fu, Ya-Ting East China Jiaotong Univ Sch Elect & Elect Engn Nanchang Jiangxi Peoples R China Key Lab Adv Control & Optimizat Jiangxi Prov Nanchang Jiangxi Peoples R China

ISBN: (纸本)9781479958252

The electric multiple units (EMU) provide a transport service in the dynamical running environment, which should meet the requirements of safety, punctuality, precise train stopping, energy conservation and comfort simultaneously. However, since pervious manual operation method of EMU is mainly based on a given V-S curve (velocity versus position curve) and drivers' experience, it cannot meet the multi-objective operation requirements in real time. In order to improve the operation strategy, this paper develops a multiobjective online optimization model for the EMU operation based on speed limit curve. Then, we optimize the operation strategy using a modified multi-objective particle swarm optimization algorithm on line, so as to obtain the Pareto optimal solution set. Further, based on the delay state of EMU running process, we pick out the optimal operation strategy from the Pareto set. Finally, the running process of the EMU operated on the optimal operation strategy can satisfy the multi-objective requirements. And the experimental results on the field data of CRH380AL (China railway high-speed EMU type-380AL) running process show the real time effectiveness of the proposed approach.

关键词： Electric multiple units Speed limit Operation strategy Multi-objective online optimization particle swarm optimization algorithm

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