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Abstract
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Article Information:
A BP Neural Network Based on Improved Particle Swarm Optimization and its Application in Reliability Forecasting
Heqing Li and Qing Tan
Corresponding Author: Heqing Li
Submitted: October 30, 2012
Accepted: December 21, 2012
Published: July 05, 2013 |
Abstract:
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The basic Particle Swarm Optimization (PSO) algorithm and its principle have been introduced, the Particle Swarm Optimization has low accelerate speed and can be easy to fall into local extreme value, so the Particle Swarm Optimization based on the improved inertia weight is presented. This method means using nonlinear decreasing weight factor to change the fundamental ways of PSO. To allow full play to the approximation capability of the function of BP neural network and overcome the main shortcomings of its liability to fall into local extreme value and the study proposed a concept of applying improved PSO algorithm and BP network jointly to optimize the original weight and threshold value of network and incorporating the improved PSO algorithm into BP network to establish a improved PSO-BP network system. This method improves convergence speed and the ability to search optimal value. We apply the improved particle swarm algorithm to reliability prediction. Compared with the traditional BP method, this kind of algorithm can minimize errors and improve convergence speed at the same time.
Key words: BP improvement, neural network, reliability prediction, the improved PSO, , ,
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Cite this Reference:
Heqing Li and Qing Tan, . A BP Neural Network Based on Improved Particle Swarm Optimization and its Application in Reliability Forecasting. Research Journal of Applied Sciences, Engineering and Technology, (07): 1246-1251.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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