BP neural network optimized with QPSO algorithm improved by DELTA potential trough and its application

doi:10.7523/j.issn.2095-6134.2015.03.019

Abstract

Abstract:

To improve the generalization ability of BP network for prediction, a BP neural network optimized with QPSO is proposed. This model uses the QPSO improved by δ potential trough to optimize the initial values of weights and thresholds of BP network. Then the data of each year's GDP are selected in training and prediction. The experiments show that the QPSO-BP network optimized by using δ potential trough produces stable prediction results. Compared with the prediction models of PSO-BP and BP, the proposed model has a better generalization ability and a higher accuracy. In addition, the calculation results of the improved QPSO-BP optimization algorithm model have smaller relative errors and average errors compared with the results of the models in the literature.

Key words: back-propagation neural network, PSO model, QPSO model, δ potential trough, GDP

CLC Number:

TP183
F201

YU Fengling, ZHOU Yang, CHEN Jianhong, ZHOU Hanling. BP neural network optimized with QPSO algorithm improved by DELTA potential trough and its application[J]. , 2015, 32(3): 416-421.

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