NDLI: An Improved Grey Neural Network Model with GA Optimization

Content Provider	IEEE Xplore Digital Library
Author	Huafen Yang Zuyuan Yang
Copyright Year	2012
Abstract	Although the grey forecasting model has been successfully employed in various fields and demonstraed promising results, its performance still could be improved. In order to improve the fitting capability of grey model, an improved grey neural network model with GA optimization is proposed in this paper. To avoid the premature convergence and inbreeding, an improved GA (IGA) is proposed in this paper. This IGA is used to improve the grey neural network. Binary encoding and random uniform distribution are employed in the course of initializing population with the purpose of increasing the population diversity, which speed up optimizing. Adaptive probability of crossover and mutation are used to avoid the population getting into local optimum and they change with the change of fitness of population. The initial condition of grey model is not consistent with theory. The improved GA is used to optimize the paramerter of the improved GM proposed in this paper. Simulation experiment indicates the improved model is effective in increasing prediction precision.
Starting Page	72
Ending Page	75
File Size	456241
Page Count	4
File Format	PDF
ISBN	9781467304702
DOI	10.1109/ICICTA.2012.25
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-01-12
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	random uniform distribution genetic algorithm Adaptation models grey model Artificial neural networks Predictive models Data models Mathematical model Forecasting neural network Genetic algorithms
Content Type	Text
Resource Type	Article

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