Groundwater is important for managing the water supply in agricultural countries like Bangladesh. Therefore, the ability to predict the changes of groundwater level is necessary for jointly planning the uses of ground...Groundwater is important for managing the water supply in agricultural countries like Bangladesh. Therefore, the ability to predict the changes of groundwater level is necessary for jointly planning the uses of groundwater resources. In this study, a new nonlinear autoregressive with exogenous inputs(NARX) network has been applied to simulate monthly groundwater levels in a well of Sylhet Sadar at a local scale. The Levenberg-Marquardt(LM) and Bayesian Regularization(BR) algorithms were used to train the NARX network, and the results were compared to determine the best architecture for predicting monthly groundwater levels over time. The comparison between LM and BR showed that NARX-BR has advantages over predicting monthly levels based on the Mean Squared Error(MSE), coefficient of determination(R^2), and Nash-Sutcliffe coefficient of efficiency(NSE). The results show that BR is the most accurate method for predicting groundwater levels with an error of ± 0.35 m. This method is applied to the management of irrigation water source, which provides important information for the prediction of local groundwater fluctuation at local level during a short period.展开更多
A numerical study on the multi-parameter control method based on nonlinear auto-regressive with exogenous input neural network （NARX） is presented here. Welding current was set as the input parameter; electrode disp...A numerical study on the multi-parameter control method based on nonlinear auto-regressive with exogenous input neural network （NARX） is presented here. Welding current was set as the input parameter; electrode displacement and dynamic resistance were set us the output parameters. The NARX model using these parameters was set up to simulate the multi-parameter resistance spot welding process. By comparing actual experimental data and NARX model output data, it was validated that the results from the model reflect the relationship between input parameter and output parameters correctly under the influence of many affecting factors.展开更多
针对工程造价影响因素多,预测困难且周期长等问题,通过将主成分分析和NARX(Non-linear Auto-Regressive with Exogenous inputs)神经网络相结合,提出一种新型基于混合算法的市政工程造价预测方法。利用主成分分析对影响市政工程造价的...针对工程造价影响因素多,预测困难且周期长等问题,通过将主成分分析和NARX(Non-linear Auto-Regressive with Exogenous inputs)神经网络相结合,提出一种新型基于混合算法的市政工程造价预测方法。利用主成分分析对影响市政工程造价的主要影响因素进行原始数据处理,消除其相关性,可有效降低数据冗余,也降低神经网络运算易出现局部极小点概率。以主成分分析数据作为输入,单位面积工程造价作为输出,采用贝叶斯正则化算法构建的NARX网络神经模型进行市政工程造价预测。算例结果表明,基于PCA与NARX进行市政工程造价快速、准确,证明预测有效可行。展开更多
文摘Groundwater is important for managing the water supply in agricultural countries like Bangladesh. Therefore, the ability to predict the changes of groundwater level is necessary for jointly planning the uses of groundwater resources. In this study, a new nonlinear autoregressive with exogenous inputs(NARX) network has been applied to simulate monthly groundwater levels in a well of Sylhet Sadar at a local scale. The Levenberg-Marquardt(LM) and Bayesian Regularization(BR) algorithms were used to train the NARX network, and the results were compared to determine the best architecture for predicting monthly groundwater levels over time. The comparison between LM and BR showed that NARX-BR has advantages over predicting monthly levels based on the Mean Squared Error(MSE), coefficient of determination(R^2), and Nash-Sutcliffe coefficient of efficiency(NSE). The results show that BR is the most accurate method for predicting groundwater levels with an error of ± 0.35 m. This method is applied to the management of irrigation water source, which provides important information for the prediction of local groundwater fluctuation at local level during a short period.
文摘A numerical study on the multi-parameter control method based on nonlinear auto-regressive with exogenous input neural network （NARX） is presented here. Welding current was set as the input parameter; electrode displacement and dynamic resistance were set us the output parameters. The NARX model using these parameters was set up to simulate the multi-parameter resistance spot welding process. By comparing actual experimental data and NARX model output data, it was validated that the results from the model reflect the relationship between input parameter and output parameters correctly under the influence of many affecting factors.
文摘针对工程造价影响因素多,预测困难且周期长等问题,通过将主成分分析和NARX(Non-linear Auto-Regressive with Exogenous inputs)神经网络相结合,提出一种新型基于混合算法的市政工程造价预测方法。利用主成分分析对影响市政工程造价的主要影响因素进行原始数据处理,消除其相关性,可有效降低数据冗余,也降低神经网络运算易出现局部极小点概率。以主成分分析数据作为输入,单位面积工程造价作为输出,采用贝叶斯正则化算法构建的NARX网络神经模型进行市政工程造价预测。算例结果表明,基于PCA与NARX进行市政工程造价快速、准确,证明预测有效可行。
