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
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;
Conference_Titel :
Information Technology and Computer Science, 2009. ITCS 2009. International Conference on
Conference_Location :
Kiev
Print_ISBN :
978-0-7695-3688-0
DOI :
10.1109/ITCS.2009.54