Evolutionary artificial neural network approach for predicting properties of Cu-15Ni-8Sn-0.4Si alloy

【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.更多还原

【Abstract】 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.更多还原