摘要
A novel data mining approach,based on artificial neural network(ANN) using differential evolution(DE) training algorithm,was proposed to model the non-linear relationship between parameters of aging processes and mechanical and electrical properties of Cu-15Ni-8Sn-0.4Si alloy.In order to improve predictive accuracy of ANN model,the leave-one-out-cross-validation (LOOCV) technique was adopted to automatically determine the optimal number of neurons of the hidden layer.The forecasting performance of the proposed global optimization algorithm was compared with that of local optimization algorithm.The present calculated results are consistent with the experimental values,which suggests that the proposed evolutionary artificial neural network algorithm is feasible and efficient.Moreover,the experimental results illustrate that the DE training algorithm combined with gradient-based training algorithm achieves better convergence performance and the lowest forecasting errors and is therefore considered to be a promising alternative method to forecast the hardness and electrical conductivity of Cu-15Ni-8Sn-0.4Si alloy.
A novel data mining approach, based on artificial neural network（ANN） using differential evolution（DE） training algorithm, was proposed to model the non-linear relationship between parameters of aging processes and mechanical and electrical properties of Cu-15Ni-8Sn-0.4Si alloy. In order to improve predictive accuracy of ANN model, the leave-one-out-cross-validation （LOOCV） technique was adopted to automatically determine the optimal number of neurons of the hidden layer. The forecasting performance of the proposed global optimization algorithm was compared with that of local optimization algorithm. The present calculated results are consistent with the experimental values, which suggests that the proposed evolutionary artificial neural network algorithm is feasible and efficient. Moreover, the experimental results illustrate that the DE training algorithm combined with gradient-based training algorithm achieves better convergence performance and the lowest forecasting errors and is therefore considered to be a promising alternative method to forecast the hardness and electrical conductivity of Cu- 15Ni-8Sn-0.4Si alloy.
出处
《中国有色金属学会会刊：英文版》
EI
CSCD
2008年第5期1223-1228,共6页
Transactions of Nonferrous Metals Society of China
基金
Project(2002AA302505) supported by the Hi-tech Research and Development Program of China
关键词
Cu-15Ni-8Sn-0.4Si
合金
老化过程
电性质
人工神经网络
Cu-15Ni-8Sn-0.4Si alloy
electrical property
aging process
artificial neural network
differential evolution
leave-oneout-cross-validation
