A Forecasting Model of RBF Neural Network Based on Particle Swarm Optimization

In order to improve the precision of gas emission forecasting,this paper proposes a new forecasting model based on Particle Swarm Optimization (PSO).PSO is a novel random optimization method which has extensive capability of global optimization.In the model, PSO is used to optimize the weight,width and center of RBF neural network and the optimal model is applied to forecast gas emission.The diversified factors analysised with grey correlation,MATLAB is employed to implement the model for gas emission forecasting.The simulation results show that the gas emission model optimized by PSO is more accurate than the traditional RBF model．

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Particle Swarm Optimization-Based RBF Neural Network Load Forecasting Model

2009 Asia-Pacific Power and Energy Engineering Conference ◽

10.1109/appeec.2009.4918588 ◽

2009 ◽

Cited By ~ 7

Author(s):

Ning Lu ◽

Jianzhong Zhou

Keyword(s):

Neural Network ◽

Particle Swarm Optimization ◽

Rbf Neural Network ◽

Load Forecasting ◽

Particle Swarm ◽

Forecasting Model ◽

Swarm Optimization ◽

Network Load

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Marine traffic accident prediction based on particle swarm optimization-based RBF neural network

2011 3rd International Conference on Computer Research and Development ◽

10.1109/iccrd.2011.5764052 ◽

2011 ◽

Cited By ~ 1

Author(s):

Zhen-Qi Yang

Keyword(s):

Neural Network ◽

Particle Swarm Optimization ◽

Traffic Accident ◽

Rbf Neural Network ◽

Particle Swarm ◽

Swarm Optimization ◽

Accident Prediction ◽

Marine Traffic

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Transformer Internal Insulation Fault Diagnosis Based on RBF Neural Network Evolved by Immune Particle Swarm Optimization

Proceedings of 2016 Chinese Intelligent Systems Conference - Lecture Notes in Electrical Engineering ◽

10.1007/978-981-10-2338-5_9 ◽

2016 ◽

pp. 89-100 ◽

Cited By ~ 1

Author(s):

Hao Li ◽

Fuzhong Wang ◽

Rui Wang

Keyword(s):

Neural Network ◽

Particle Swarm Optimization ◽

Fault Diagnosis ◽

Rbf Neural Network ◽

Particle Swarm ◽

Swarm Optimization

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Training RBF Neural Network with Hybrid Particle Swarm Optimization

Advances in Neural Networks - ISNN 2006 - Lecture Notes in Computer Science ◽

10.1007/11759966_86 ◽

2006 ◽

pp. 577-583 ◽

Cited By ~ 10

Author(s):

Haichang Gao ◽

Boqin Feng ◽

Yun Hou ◽

Li Zhu

Keyword(s):

Neural Network ◽

Particle Swarm Optimization ◽

Rbf Neural Network ◽

Particle Swarm ◽

Swarm Optimization ◽

Hybrid Particle ◽

Hybrid Particle Swarm Optimization

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New Neural Network Inverse Control of Two-Motor Drive System

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.416-417.447 ◽

2013 ◽

Vol 416-417 ◽

pp. 447-453

Author(s):

Mei Kang ◽

Wen Xiang Zhao ◽

Jing Hua Ji ◽

Guo Hai Liu

Keyword(s):

Neural Network ◽

Particle Swarm Optimization ◽

Motor System ◽

Rbf Neural Network ◽

Particle Swarm ◽

Drive System ◽

Motor Drive ◽

Swarm Optimization ◽

Synchronous System ◽

Motor Drive System

Two-motor drive system is a multi-variable, nonlinear and strongly coupled system. A new synchronous control strategy for two-motor system is proposed based on radial basis function (RBF) neural network inverse with particle swarm optimization. To enhance the system performance, the particle swarm optimization is adopted to optimize the RBF nerve center, an optimized RBF neural network inverse and a two-motor system is connected in series to form composite pseudo-linear system. This two-motor synchronous system can be decoupled into two independent linear subsystems for speed and tension. Then, the decoupled control is implemented by designing a linear closed-loop adjustor. The experimental results verify that the two-motor synchronous system can be decoupled well for speed and tension based on the proposed neural network inverse system. Also, the proposed system can deal with external disturbances with strong robustness.

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Application of Particle Swarm Optimization and RBF Neural Network in Fault Diagnosis of Analogue Circuits

2009 Third International Symposium on Intelligent Information Technology Application ◽

10.1109/iita.2009.382 ◽

2009 ◽

Cited By ~ 1

Author(s):

Ye Ming

Keyword(s):

Neural Network ◽

Particle Swarm Optimization ◽

Fault Diagnosis ◽

Rbf Neural Network ◽

Particle Swarm ◽

Swarm Optimization ◽

Analogue Circuits

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Algorithm Researching of RBF Neural Network Based on Improved PSO

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.179-180.233 ◽

2011 ◽

Vol 179-180 ◽

pp. 233-238 ◽

Cited By ~ 3

Author(s):

Hua Chen ◽

Yi Ren Fan ◽

Shao Gui Deng

Keyword(s):

Neural Network ◽

Particle Swarm Optimization ◽

Rbf Neural Network ◽

Particle Swarm ◽

Parameter Vector ◽

Swarm Optimization ◽

Improved Particle Swarm Optimization ◽

Whole Space ◽

Best Parameter ◽

Improved Pso

In view of the defect of particle swarm optimization which easily gets into partial extremum, the paper put out an improved particle swarm optimization, and applies the algorithm to the selecting of parameter of RBF neural network basal function. It searches the best parameter vector in the whole space, according to coding means, iterative formula, adapted function which the paper puts forwards. The experiment proves that RBF neural network based on improved PSO has faster convergent speed, and higher error precision.

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