Nonlinear resistivity inversion requires efficient artificial neural network(ANN)model for better inversion results.An evolutionary BP neural network(BPNN)approach based on differential evolution(DE)algorithm was pres...Nonlinear resistivity inversion requires efficient artificial neural network(ANN)model for better inversion results.An evolutionary BP neural network(BPNN)approach based on differential evolution(DE)algorithm was presented,which was able to improve global search ability for resistivity tomography 2-D nonlinear inversion.In the proposed method,Tent equation was applied to obtain automatic parameter settings in DE and the restricted parameter Fcrit was used to enhance the ability of converging to global optimum.An implementation of proposed DE-BPNN was given,the network had one hidden layer with 52 nodes and it was trained on 36 datasets and tested on another 4 synthetic datasets.Two abnormity models were used to verify the feasibility and effectiveness of the proposed method,the results show that the proposed DE-BP algorithm has better performance than BP,conventional DE-BP and other chaotic DE-BP methods in stability and accuracy,and higher imaging quality than least square inversion.展开更多
In this paper we prove a finite convergence of online BP algorithms for nonlinear feedforward neural networks when the training patterns are linearly separable.
Although time series are frequently nonlinear in reality, people tend to use linear models to fit them under some assumptJLons unnecessarily in accordance with the truth, which unsurprisingly leads to unsatisfactory p...Although time series are frequently nonlinear in reality, people tend to use linear models to fit them under some assumptJLons unnecessarily in accordance with the truth, which unsurprisingly leads to unsatisfactory performance. This paper proposes a forecast method： Genetic programming based on least square method （GP-LSM）. Inheriting the advantages of genetic algorithm （GA）, without relying on the particular distribution of the data, this method can improve the prediction accuracy because of its ability of fitting nonlinear models, and raise the convergence speed benefitting from the least square method （LSM）. In order to verify the vMidity of this method, the authors compare this method with seasonal auto regression integrated moving average （SARIMA） and back propagation artificial neural networks （BP-ANN）. The results of empirical analysis show that forecast accuracy and direction prediction accuracy of GP-LSM are obviously better than those of the others.展开更多
基金Project(20120162110015)supported by the Research Fund for the Doctoral Program of Higher Education,ChinaProject(41004053)supported by the National Natural Science Foundation of ChinaProject(12c0241)supported by Scientific Research Fund of Hunan Provincial Education Department,China
文摘Nonlinear resistivity inversion requires efficient artificial neural network(ANN)model for better inversion results.An evolutionary BP neural network(BPNN)approach based on differential evolution(DE)algorithm was presented,which was able to improve global search ability for resistivity tomography 2-D nonlinear inversion.In the proposed method,Tent equation was applied to obtain automatic parameter settings in DE and the restricted parameter Fcrit was used to enhance the ability of converging to global optimum.An implementation of proposed DE-BPNN was given,the network had one hidden layer with 52 nodes and it was trained on 36 datasets and tested on another 4 synthetic datasets.Two abnormity models were used to verify the feasibility and effectiveness of the proposed method,the results show that the proposed DE-BP algorithm has better performance than BP,conventional DE-BP and other chaotic DE-BP methods in stability and accuracy,and higher imaging quality than least square inversion.
基金the National Natural science Foundation of China (10471017)the Basic Research Program of the National Defence Committee of Science,Technology and Industry of China (K1400060406)
文摘In this paper we prove a finite convergence of online BP algorithms for nonlinear feedforward neural networks when the training patterns are linearly separable.
基金supported by the National Natural Science Foundation of China under Grant Nos.71171011 and 91224001Program for New Century Excellent Talents in University(NCET-12-0756)
文摘Although time series are frequently nonlinear in reality, people tend to use linear models to fit them under some assumptJLons unnecessarily in accordance with the truth, which unsurprisingly leads to unsatisfactory performance. This paper proposes a forecast method： Genetic programming based on least square method （GP-LSM）. Inheriting the advantages of genetic algorithm （GA）, without relying on the particular distribution of the data, this method can improve the prediction accuracy because of its ability of fitting nonlinear models, and raise the convergence speed benefitting from the least square method （LSM）. In order to verify the vMidity of this method, the authors compare this method with seasonal auto regression integrated moving average （SARIMA） and back propagation artificial neural networks （BP-ANN）. The results of empirical analysis show that forecast accuracy and direction prediction accuracy of GP-LSM are obviously better than those of the others.
