DocumentCode :
2290388
Title :
A hybrid PSO-BP algorithm and its application
Author :
Hu, Jie ; Zeng, Xiangjin
Author_Institution :
Sch. of Sci., Wuhan Univ. of Technol., Wuhan, China
Volume :
5
fYear :
2010
fDate :
10-12 Aug. 2010
Firstpage :
2520
Lastpage :
2523
Abstract :
An approach that neural network optimized with PSO algorithm is proposed in the paper. Unlike conventional training method with gradient descent method only, this paper introduces a hybrid training algorithm by combining the PSO and BP algorithm. The PSO is used to optimize the initial parameters of the BP neural network, including the weights and biases. It can effectively better the cases that network is easily trapped to a local optimum and has a slow velocity of convergence. The experiment results show the method in the paper has greater improvement in both accuracy and velocity of convergence for BP neural network.
Keywords :
backpropagation; learning (artificial intelligence); neural nets; particle swarm optimisation; road traffic; BP neural network; convergence; gradient descent method; hybrid PSO-BP algorithm; hybrid training algorithm; Artificial neural networks; Forecasting; Heuristic algorithms; Particle swarm optimization; Prediction algorithms; Signal processing algorithms; Training; BP algorithm; Feedforward neural network; Particle swarm optimization algorithm;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Natural Computation (ICNC), 2010 Sixth International Conference on
Conference_Location :
Yantai, Shandong
Print_ISBN :
978-1-4244-5958-2
Type :
conf
DOI :
10.1109/ICNC.2010.5583289
Filename :
5583289
Link To Document :
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