DocumentCode
496108
Title
Mine Forecast Based on Genetic Annealing Neural Network
Author
Shao, Yuxiang ; Zhang, Dongmei
Author_Institution
Sch. of Comput. Sci. & Technol., China Univ. of Geosci., Wuhan, China
Volume
1
fYear
2009
fDate
25-26 July 2009
Firstpage
241
Lastpage
244
Abstract
Since the BP neural network algorithm has some unavoidable disadvantages, such as slowly converging speed and easily running into local infinitesimal, the genetic algorithm and simulated annealing algorithm with the overall search capability has been put forward to optimize authority value and threshold value of BP nerve network. In this paper, GA-SA neural network algorithm model has been established and applied into the mine forecast. The result shows that this model has significant advantages inspect of fast convergence speed, good generalization ability and not easy to yield minimal local results. In generally, this model exhibits good representation and strong prediction ability, and is a helpful tool in the future mine prediction.
Keywords
backpropagation; genetic algorithms; mining; mining industry; neural nets; simulated annealing; BP neural network; GA-SA neural network; authority value; fast convergence speed; genetic algorithm; genetic annealing neural network; good generalization ability; mine forecast; overall search capability; simulated annealing; threshold value; Appraisal; Computer science; Convergence; Genetic algorithms; Geology; Mineralization; Neural networks; Predictive models; Simulated annealing; Statistical distributions; BP neural network; genetic algorithm; mine forecast; simulated annealing algorithm;
fLanguage
English
Publisher
ieee
Conference_Titel
Information Technology and Computer Science, 2009. ITCS 2009. International Conference on
Conference_Location
Kiev
Print_ISBN
978-0-7695-3688-0
Type
conf
DOI
10.1109/ITCS.2009.54
Filename
5190060
Link To Document