In order to overcome shortcomings of traditional BP neural network,such as low study efficiency, slow convergence speed,easily trapped into local optimal solution,we proposed an improved BP neural network model based ...In order to overcome shortcomings of traditional BP neural network,such as low study efficiency, slow convergence speed,easily trapped into local optimal solution,we proposed an improved BP neural network model based on adaptive particle swarm optimization( PSO) algorithm. This algorithm adjusted the inertia weight coefficients and learning factors adaptively and therefore could be used to optimize the weights in the BP network. After establishing the improved PSO-BP( IPSO-BP) model,it was applied to solve fault diagnosis of rolling bearing. Wavelet denoising was selected to reduce the noise of the original vibration signals,and based on these vibration signals a wide set of features were used as the inputs in the neural network models. We demonstrate the effectiveness of the proposed approach by comparing with the traditional BP,PSO-BP and linear PSO-BP( LPSO-BP) algorithms. The experimental results show that IPSO-BP network outperforms other algorithms with faster convergence speed,lower errors,higher diagnostic accuracy and learning ability.展开更多
Firstly,the early warning index system of coal mine safety production was given from four aspects as personnel,environment,equipment and management.Then,improvement measures which are additional momentum method,adapti...Firstly,the early warning index system of coal mine safety production was given from four aspects as personnel,environment,equipment and management.Then,improvement measures which are additional momentum method,adaptive learning rate,particle swarm optimization algorithm,variable weight method and asynchronous learning factor,are used to optimize BP neural network models.Further,the models are applied to a comparative study on coal mine safety warning instance.Results show that the identification precision of MPSO-BP network model is higher than GBP and PSO-BP model,and MPSOBP model can not only effectively reduce the possibility of the network falling into a local minimum point,but also has fast convergence and high precision,which will provide the scientific basis for the forewarning management of coal mine safety production.展开更多
针对日趋严重的电网谐波污染亟需大量谐波数据支撑分析和治理及电网谐波监测能力不足的问题,提出一种改进减法平均优化(subtraction average based optimizer, SABO)算法优化反向传播(back-propagation, BP)神经网络实现谐波预测,以缓...针对日趋严重的电网谐波污染亟需大量谐波数据支撑分析和治理及电网谐波监测能力不足的问题,提出一种改进减法平均优化(subtraction average based optimizer, SABO)算法优化反向传播(back-propagation, BP)神经网络实现谐波预测,以缓解当前谐波数据匮乏的问题。为了克服现有SABO算法易于陷入局部最优解,初始化时使用Logistic混沌映射替代随机数,同时迭代搜索中利用黄金正弦优化算法辅助SABO跳出局部最优,从而提高BP神经网络预测准确率。最后,以某省实际运行数据验证所提改进SABAO-BP模型在谐波电压畸变率及单次谐波电压含有率预测中均具有较高准确性。展开更多
The existence of soil macropores is a common phenomenon.Due to the existence of soil macropores,the amount of solute loss carried by water is deeply modified,which affects watershed hydrologic response.In this study,a...The existence of soil macropores is a common phenomenon.Due to the existence of soil macropores,the amount of solute loss carried by water is deeply modified,which affects watershed hydrologic response.In this study,a new improved BP(Back Propagation)neural network method,using Levenberg–Marquand training algorithm,was used to analyze the solute loss on slopes taking into account the soil macropores.The rainfall intensity,duration,the slope,the characteristic scale of macropores and the adsorption coefficient of ions,are used as the variables of network input layer.The network middle layer is used as hidden layer,the number of hidden nodes is five,and a tangent transfer function is used as its neurons transfer function.The cumulative solute loss on the slope is used as the variable of network output layer.A linear transfer function is used as its neurons transfer function.Artificial rainfall simulation experiments are conducted in indoor experimental tanks in order to verify this model.The error analysis and the performance comparison between the proposed method and traditional gradient descent method are done.The results show that the convergence rate and the prediction accuracy of the proposed method are obviously higher than that of traditional gradient descent method.In addition,using the experimental data,the influence of soil macropores on slope solute loss has been further confirmed before the simulation.展开更多
基金Sponsored by the National Natural Science Foundation of China(Grant No.61174115 and 51104044)
文摘In order to overcome shortcomings of traditional BP neural network,such as low study efficiency, slow convergence speed,easily trapped into local optimal solution,we proposed an improved BP neural network model based on adaptive particle swarm optimization( PSO) algorithm. This algorithm adjusted the inertia weight coefficients and learning factors adaptively and therefore could be used to optimize the weights in the BP network. After establishing the improved PSO-BP( IPSO-BP) model,it was applied to solve fault diagnosis of rolling bearing. Wavelet denoising was selected to reduce the noise of the original vibration signals,and based on these vibration signals a wide set of features were used as the inputs in the neural network models. We demonstrate the effectiveness of the proposed approach by comparing with the traditional BP,PSO-BP and linear PSO-BP( LPSO-BP) algorithms. The experimental results show that IPSO-BP network outperforms other algorithms with faster convergence speed,lower errors,higher diagnostic accuracy and learning ability.
文摘Firstly,the early warning index system of coal mine safety production was given from four aspects as personnel,environment,equipment and management.Then,improvement measures which are additional momentum method,adaptive learning rate,particle swarm optimization algorithm,variable weight method and asynchronous learning factor,are used to optimize BP neural network models.Further,the models are applied to a comparative study on coal mine safety warning instance.Results show that the identification precision of MPSO-BP network model is higher than GBP and PSO-BP model,and MPSOBP model can not only effectively reduce the possibility of the network falling into a local minimum point,but also has fast convergence and high precision,which will provide the scientific basis for the forewarning management of coal mine safety production.
文摘针对日趋严重的电网谐波污染亟需大量谐波数据支撑分析和治理及电网谐波监测能力不足的问题,提出一种改进减法平均优化(subtraction average based optimizer, SABO)算法优化反向传播(back-propagation, BP)神经网络实现谐波预测,以缓解当前谐波数据匮乏的问题。为了克服现有SABO算法易于陷入局部最优解,初始化时使用Logistic混沌映射替代随机数,同时迭代搜索中利用黄金正弦优化算法辅助SABO跳出局部最优,从而提高BP神经网络预测准确率。最后,以某省实际运行数据验证所提改进SABAO-BP模型在谐波电压畸变率及单次谐波电压含有率预测中均具有较高准确性。
文摘针对传统BP神经网络估算电池SOC过程中,存在初始权值和阈值对预测精度影响较大的问题,引入Tent混沌映射和自适应收敛因子对灰狼算法(GWO)进行改进,改善灰狼算法易陷入局部最优、后期迭代效率不高的缺点。将改进灰狼算法(improved grey Wolf algorithm,IGWO)与BP神经网络模型结合,得到BP神经网络最优初始权值和阈值,提高预测精度和收敛速度。对锂电池充放电实验数据预处理,得到样本数据。利用MATLAB进行仿真验证,结果表明,IGWO-BP神经网络算法的预测精度相较于传统BP神经网络算法、GWO-BP神经网络算法更优,基于改进灰狼优化BP神经网络估算电池SOC的方法的绝对误差能控制在1.53%以内,有效提高了预测精度和收敛速度。
基金This research was financially supported by the National Natural Science Foundation of China(No.41301037)the Natural Science Foundation of the Jiangsu Higher Education Institutions of China(No.11KJB170008)Innovation and Entrepreneurship Training Program for College Students in Jiangsu Province(No.201910300106Y).For the help in carrying out the experiments,I wish to thank for Professor Rui Xiaofang,Hohai University,China.
文摘The existence of soil macropores is a common phenomenon.Due to the existence of soil macropores,the amount of solute loss carried by water is deeply modified,which affects watershed hydrologic response.In this study,a new improved BP(Back Propagation)neural network method,using Levenberg–Marquand training algorithm,was used to analyze the solute loss on slopes taking into account the soil macropores.The rainfall intensity,duration,the slope,the characteristic scale of macropores and the adsorption coefficient of ions,are used as the variables of network input layer.The network middle layer is used as hidden layer,the number of hidden nodes is five,and a tangent transfer function is used as its neurons transfer function.The cumulative solute loss on the slope is used as the variable of network output layer.A linear transfer function is used as its neurons transfer function.Artificial rainfall simulation experiments are conducted in indoor experimental tanks in order to verify this model.The error analysis and the performance comparison between the proposed method and traditional gradient descent method are done.The results show that the convergence rate and the prediction accuracy of the proposed method are obviously higher than that of traditional gradient descent method.In addition,using the experimental data,the influence of soil macropores on slope solute loss has been further confirmed before the simulation.
