An Improved BP Algorithm and its Application

Hong Chun Wang; Feng  Song; Feng Wen  Wen

doi:10.4028/www.scientific.net/AMR.765-767.489

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Clustering Algorithm Based on Improved Particle Swarm Optimization
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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 765-767An Improved BP Algorithm and its Application

An Improved BP Algorithm and its Application

Abstract:

in order to solve the problem which standard BP algorithm didnt have a good prediction accuracy for testing samples, a L-M Bayesian regularization algorithm was proposed by improved standard BP algorithm and applied to predict the resident consumption level of Cheng du. The experimental results show the L-M Bayesian regularization algorithm neural network has a higher accuracy, a more stable performance and a stronger generalization ability than another two improved algorithms in the same conditions and has a very good effect for the forecast of resident consumption level.

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Periodical:

Advanced Materials Research (Volumes 765-767)

Pages:

489-492

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.765-767.489

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Online since:

September 2013

Authors:

Hong Chun Wang, Feng Song, Feng Wen Wen

Keywords:

Back Propagation Algorithm (BPA), Bayesian Regularization Algorithm, Levenberg-Marquardt (LM) Optimization Algorithm

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