将改进遗传算法(GA)和误差反向传播(BP)算法相结合构成的混合算法用于训练人工神经网络。该混合算法有效地解决了常规 BP 算法学习网络权值收敛速度慢、易陷入局部极小和 GA 算法独立训练神经网络速度缓慢等缺点,并对其应用于电力变压...将改进遗传算法(GA)和误差反向传播(BP)算法相结合构成的混合算法用于训练人工神经网络。该混合算法有效地解决了常规 BP 算法学习网络权值收敛速度慢、易陷入局部极小和 GA 算法独立训练神经网络速度缓慢等缺点,并对其应用于电力变压器故障诊断进行了仿真,仿真结果表明了该算法具有较快的收敛速度和较高的计算精度,故障诊断结果证实了该算法应用于电力变压器故障诊断的有效性。展开更多
In order to analyze the influence rule of experimental parameters on the energy-absorption characteristics and effectively forecast energy-absorption characteristic of thin-walled structure, the forecast model of GA-B...In order to analyze the influence rule of experimental parameters on the energy-absorption characteristics and effectively forecast energy-absorption characteristic of thin-walled structure, the forecast model of GA-BP hybrid algorithm was presented by uniting respective applicability of back-propagation artificial neural network （BP-ANN） and genetic algorithm （GA）. The detailed process was as follows. Firstly, the GA trained the best weights and thresholds as the initial values of BP-ANN to initialize the neural network. Then, the BP-ANN after initialization was trained until the errors converged to the required precision. Finally, the network model, which met the requirements after being examined by the test samples, was applied to energy-absorption forecast of thin-walled cylindrical structure impacting. After example analysis, the GA-BP network model was trained until getting the desired network error only by 46 steps, while the single BP-ANN model achieved the same network error by 992 steps, which obviously shows that the GA-BP hybrid algorithm has faster convergence rate. The average relative forecast error （ARE） of the SEA predictive results obtained by GA-BP hybrid algorithm is 1.543%, while the ARE of the SEA predictive results obtained by BP-ANN is 2.950%, which clearly indicates that the forecast precision of the GA-BP hybrid algorithm is higher than that of the BP-ANN.展开更多
文摘将改进遗传算法(GA)和误差反向传播(BP)算法相结合构成的混合算法用于训练人工神经网络。该混合算法有效地解决了常规 BP 算法学习网络权值收敛速度慢、易陷入局部极小和 GA 算法独立训练神经网络速度缓慢等缺点,并对其应用于电力变压器故障诊断进行了仿真,仿真结果表明了该算法具有较快的收敛速度和较高的计算精度,故障诊断结果证实了该算法应用于电力变压器故障诊断的有效性。
基金Project(50175110) supported by the National Natural Science Foundation of ChinaProject(2009bsxt019) supported by the Graduate Degree Thesis Innovation Foundation of Central South University, China
文摘In order to analyze the influence rule of experimental parameters on the energy-absorption characteristics and effectively forecast energy-absorption characteristic of thin-walled structure, the forecast model of GA-BP hybrid algorithm was presented by uniting respective applicability of back-propagation artificial neural network （BP-ANN） and genetic algorithm （GA）. The detailed process was as follows. Firstly, the GA trained the best weights and thresholds as the initial values of BP-ANN to initialize the neural network. Then, the BP-ANN after initialization was trained until the errors converged to the required precision. Finally, the network model, which met the requirements after being examined by the test samples, was applied to energy-absorption forecast of thin-walled cylindrical structure impacting. After example analysis, the GA-BP network model was trained until getting the desired network error only by 46 steps, while the single BP-ANN model achieved the same network error by 992 steps, which obviously shows that the GA-BP hybrid algorithm has faster convergence rate. The average relative forecast error （ARE） of the SEA predictive results obtained by GA-BP hybrid algorithm is 1.543%, while the ARE of the SEA predictive results obtained by BP-ANN is 2.950%, which clearly indicates that the forecast precision of the GA-BP hybrid algorithm is higher than that of the BP-ANN.
