DocumentCode :
3332269
Title :
Radial basis function network wind tunnel wind speed detection algorithm based on PSO
Author :
Chen Baoyuan ; Sun Chenlin ; Wu Qian ; Lin Xirong
Author_Institution :
Higher Educ. key Lab. for Meas. & Control Technol. & Instrumentations of Heilongjiang Province, Harbin Univ. of Sci. & Technol., Harbin, China
Volume :
2
fYear :
2011
fDate :
22-24 Aug. 2011
Firstpage :
1230
Lastpage :
1234
Abstract :
In common with the method of wind speed measuring, environmental factors is not consideration enough that led the measurement of the accuracy is not high. In order to solve these problems, and put forward the wind measurement based on radial basis function (RBF) network of particle swarm optimization (PSO). This paper is selected RBF neural network constructed corresponding model based on PSO. The PSO-RBF network has simple network structure, fast learning methods, good generalization ability. And it compared with other methods as a distinct advantage. Through simulation of MATLAB shows, it has small error that between the measured value of anemometer and the simulated value of network of based on PSO-RBF, and obvious advantage over other optimization algorithm. To know that wind measurement of based on PSO-RBF has a quick, high precision characteristics.
Keywords :
anemometers; generalisation (artificial intelligence); learning (artificial intelligence); particle swarm optimisation; radial basis function networks; velocity measurement; wind tunnels; PSO; RBF neural network; generalization; learning methods; particle swarm optimization; radial basis function network; wind measurement; wind speed detection; wind tunnel; Artificial neural networks; Atmospheric measurements; Biology; Clustering algorithms; Fluid flow measurement; Measurement uncertainty; Particle measurements; PSO; RBF; neural network; wind measurements;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Strategic Technology (IFOST), 2011 6th International Forum on
Conference_Location :
Harbin, Heilongjiang
Print_ISBN :
978-1-4577-0398-0
Type :
conf
DOI :
10.1109/IFOST.2011.6021242
Filename :
6021242
Link To Document :
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