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 mech...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.展开更多
By metallographic test, SEM, TEM and energy spectrum, the microstructure and properties of Cu 15Ni 8Sn 0.4Si alloy were studied. The results show that the added Si combines with Ni and forms Ni 3Si and Ni 2Si phases. ...By metallographic test, SEM, TEM and energy spectrum, the microstructure and properties of Cu 15Ni 8Sn 0.4Si alloy were studied. The results show that the added Si combines with Ni and forms Ni 3Si and Ni 2Si phases. During ageing at 380 ℃, the precipitation of Ni 2Si phase suppresses discontinuous precipitation to some degree. After adding Si, the conductivity and hardness of Cu 15Ni 8Sn alloy are increased to some degree.展开更多
基金Project(2002AA302505) supported by the Hi-tech Research and Development Program of China
文摘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.
文摘By metallographic test, SEM, TEM and energy spectrum, the microstructure and properties of Cu 15Ni 8Sn 0.4Si alloy were studied. The results show that the added Si combines with Ni and forms Ni 3Si and Ni 2Si phases. During ageing at 380 ℃, the precipitation of Ni 2Si phase suppresses discontinuous precipitation to some degree. After adding Si, the conductivity and hardness of Cu 15Ni 8Sn alloy are increased to some degree.
