为了更好地描述沥青混合料的时间和温度依赖性,优选表征动态力学性质的黏弹力学模型,推广黏弹性动态力学设计方法。基于线性黏弹性Kramers-Kronig近似解析式,通过对存储模量Sigmoidal函数解析式求导后,得到损失模量解析式,称为Sigmoida...为了更好地描述沥青混合料的时间和温度依赖性,优选表征动态力学性质的黏弹力学模型,推广黏弹性动态力学设计方法。基于线性黏弹性Kramers-Kronig近似解析式,通过对存储模量Sigmoidal函数解析式求导后,得到损失模量解析式,称为SigmoidalⅡ类模型。应用黏弹性材料时-温等效原理,通过构造不同目标函数,建立了上述模型黏弹函数主曲线,并与SigmoidalⅠ-Ⅰ模型、SigmoidalⅠ-Ⅱ模型进行了对比分析。结果表明:3个模型均能应用时-温等效原理建立黏弹函数(动态模量和相位角)的主曲线,与AASHTO R 62-131规范对比,3个模型均提出了相位角主曲线解析式,目标函数构造时,黏弹参数的选择影响Sigmoidal模型的拟合效果。对比另外2个模型,SigmoidalⅡ模型仅采用一个黏弹参数(动态模量)构造目标函数即可建立所有黏弹参数主曲线及Cole-Cole曲线,且黏弹函数测试值与预测值吻合较好,其中,动态模量和相位角曲线的拟合优度均在0.95以上,说明该模型能更好地描述沥青混合料的动态黏弹参数。SigmoidalⅡ模型存储模量和损失模量(动态模量和相位角)共用一套模型参数,黏弹参数之间满足线性黏弹性因果关系且符合力学模型的要求。SigmoidalⅡ模型可为沥青混合料设计和沥青路面层状黏弹动力学计算提供新的参考。展开更多
In this paper we study the degree of approximation by superpositions of a sigmoidal function.We mainly consider the univariate case.If f is a continuous function,we prove that for any bounded sigmoidal function σ,d_...In this paper we study the degree of approximation by superpositions of a sigmoidal function.We mainly consider the univariate case.If f is a continuous function,we prove that for any bounded sigmoidal function σ,d_(n,σ)(f)≤‖σ‖ω(f,1/(n+1)).For the Heaviside function H(x),we prove that d_(n,H)(f)≤ω(f,1/(2(n+1))). If f is a continuous funnction of bounded variation,we prove that d_(n,σ)(f)≤‖σ‖/(n+1)V(f)and d_(n,H)(f)≤ 1/(2(n+1))V(f).For he Heaviside function,the coefficient 1 and the approximation orders are the best possible.We compare these results with the classical Jackson and Bernstein theorems,and make some conjec- tures for further study.展开更多
In this paper, a constructive theory is developed for approximating func- tions of one or more variables by superposition of sigmoidal functions. This is done in the uniform norm as well as in the L^p norm. Results fo...In this paper, a constructive theory is developed for approximating func- tions of one or more variables by superposition of sigmoidal functions. This is done in the uniform norm as well as in the L^p norm. Results for the simultaneous approx- imation, with the same order of accuracy, of a function and its derivatives （whenever these exist）, are obtained. The relation with neural networks and radial basis func- tions approximations is discussed. Numerical examples are given for the purpose of illustration.展开更多
In this paper, we introduce a type of approximation operators of neural networks with sigmodal functions on compact intervals, and obtain the pointwise and uniform estimates of the ap- proximation. To improve the appr...In this paper, we introduce a type of approximation operators of neural networks with sigmodal functions on compact intervals, and obtain the pointwise and uniform estimates of the ap- proximation. To improve the approximation rate, we further introduce a type of combinations of neurM networks. Moreover, we show that the derivatives of functions can also be simultaneously approximated by the derivatives of the combinations. We also apply our method to construct approximation operators of neural networks with sigmodal functions on infinite intervals.展开更多
The COVID-19 pandemic with its new variants has severely affected the whole world socially and economically.This study presents a novel data analysis approach to predict the spread of COVID-19.SIR and logistic models ...The COVID-19 pandemic with its new variants has severely affected the whole world socially and economically.This study presents a novel data analysis approach to predict the spread of COVID-19.SIR and logistic models are commonly used to determine the duration at the end of the pandemic.Results show that these well-known models may provide unrealistic predictions for countries that have pandemics spread with multiple peaks and waves.A new prediction approach based on the sigmoidal transition(ST)model provided better estimates than the traditional models.In this study,a multiple-term sigmoidal transition(MTST)model was developed and validated for several countries with multiple peaks and waves.This approach proved to fit the actual data better and allowed the spread of the pandemic to be accurately tracked.The UK,Italy,Saudi Arabia,and Tunisia,which experienced several peaks of COVID-19,were used as case studies.The MTST model was validated for these countries for the data of more than 500 days.The results show that the correlating model provided good fits with regression coefficients(R2)>0.999.The estimated model parameters were obtained with narrow 95%confidence interval bounds.It has been found that the optimum number of terms to be used in the MTST model corresponds to the highest R^(2),the least RMSE,and the narrowest 95%confidence interval having positive bounds.展开更多
Reservoir landslides are significant geological hazards that pose severe risks to reservoir safety.Detecting the spatial-temporal evolution of slope movement is crucial for effective risk assessment and disaster mitig...Reservoir landslides are significant geological hazards that pose severe risks to reservoir safety.Detecting the spatial-temporal evolution of slope movement is crucial for effective risk assessment and disaster mitigation.InSAR technology has been extensively employed to monitor surface deformations in reservoir landslides.However,the accuracy of InSAR-derived deformation fields is often limited by the reliability of prior deformation model.Traditional models,which primarily rely on linear or periodic function,frequently overlook the step-like evolution characteristics of reservoir landslides.To address this limitation,this study introduces a multi-temporal InSAR approach that incorporates Sigmoid function to enhance the deformation modeling of reservoir landslides.To solve the nonlinear parameters within the model,Taylor series expansion-based observation equation is constructed to estimate these parameters accurately.The proposed model was evaluated using both the simulated and real datasets from the Hongyanzi landslide in the Pubugou reservoir area.The results demonstrate that the proposed model significantly improves the accuracies of parameter estimation and deformation time-series.Experiments conducted under the sensitivity of interferogram stacks and varying atmospheric phase screen interference magnitudes further confirm the proposed model’s robustness and application potential.In addition,the sensitivity analysis of the initial parameters in the real data experiment scenario demonstrates the robustness of the proposed model’s nonlinear parameter estimation.Finally,the cross-correlation analysis reveals that the deformation of the Hongyanzi landslide is triggered by the decline of the reservoir water level,and quantitatively evaluates the lag time between the deformation and the reservoir water level.Our results offer novel insights for InSAR monitoring of other complex deformation evolution scenarios.Prior information is incorporated into the deformation modeling to estimate a more reliable InSAR deformation field.展开更多
构造一种适用于反向传播(backpropagation,BP)神经网络的新型激活函数Lfun(logarithmic series function),并使用基于该函数的BP神经网络进行机床能耗状态的预测。首先,分析Sigmoid系列和ReLU系列激活函数的特点和缺陷,结合对数函数,构...构造一种适用于反向传播(backpropagation,BP)神经网络的新型激活函数Lfun(logarithmic series function),并使用基于该函数的BP神经网络进行机床能耗状态的预测。首先,分析Sigmoid系列和ReLU系列激活函数的特点和缺陷,结合对数函数,构造了一种非线性分段含参数激活函数。该函数可导且光滑、导数形式简单、单调递增、输出均值为零,且通过可变参数使函数形式更灵活;其次,通过数值仿真实验在公共数据集上将Lfun函数与Sigmoid、ReLU、tanh、Leaky_ReLU和ELU函数的性能进行对比;最后,使用基于Lfun函数的BP神经网络进行机床能耗状态的预测。实验结果表明,使用Lfun函数的BP神经网络相较于使用其他几种常用激活函数的网络具有更好的性能。展开更多
文摘为了更好地描述沥青混合料的时间和温度依赖性,优选表征动态力学性质的黏弹力学模型,推广黏弹性动态力学设计方法。基于线性黏弹性Kramers-Kronig近似解析式,通过对存储模量Sigmoidal函数解析式求导后,得到损失模量解析式,称为SigmoidalⅡ类模型。应用黏弹性材料时-温等效原理,通过构造不同目标函数,建立了上述模型黏弹函数主曲线,并与SigmoidalⅠ-Ⅰ模型、SigmoidalⅠ-Ⅱ模型进行了对比分析。结果表明:3个模型均能应用时-温等效原理建立黏弹函数(动态模量和相位角)的主曲线,与AASHTO R 62-131规范对比,3个模型均提出了相位角主曲线解析式,目标函数构造时,黏弹参数的选择影响Sigmoidal模型的拟合效果。对比另外2个模型,SigmoidalⅡ模型仅采用一个黏弹参数(动态模量)构造目标函数即可建立所有黏弹参数主曲线及Cole-Cole曲线,且黏弹函数测试值与预测值吻合较好,其中,动态模量和相位角曲线的拟合优度均在0.95以上,说明该模型能更好地描述沥青混合料的动态黏弹参数。SigmoidalⅡ模型存储模量和损失模量(动态模量和相位角)共用一套模型参数,黏弹参数之间满足线性黏弹性因果关系且符合力学模型的要求。SigmoidalⅡ模型可为沥青混合料设计和沥青路面层状黏弹动力学计算提供新的参考。
文摘In this paper we study the degree of approximation by superpositions of a sigmoidal function.We mainly consider the univariate case.If f is a continuous function,we prove that for any bounded sigmoidal function σ,d_(n,σ)(f)≤‖σ‖ω(f,1/(n+1)).For the Heaviside function H(x),we prove that d_(n,H)(f)≤ω(f,1/(2(n+1))). If f is a continuous funnction of bounded variation,we prove that d_(n,σ)(f)≤‖σ‖/(n+1)V(f)and d_(n,H)(f)≤ 1/(2(n+1))V(f).For he Heaviside function,the coefficient 1 and the approximation orders are the best possible.We compare these results with the classical Jackson and Bernstein theorems,and make some conjec- tures for further study.
基金supported, in part, by the GNAMPA and the GNFM of the Italian INdAM
文摘In this paper, a constructive theory is developed for approximating func- tions of one or more variables by superposition of sigmoidal functions. This is done in the uniform norm as well as in the L^p norm. Results for the simultaneous approx- imation, with the same order of accuracy, of a function and its derivatives （whenever these exist）, are obtained. The relation with neural networks and radial basis func- tions approximations is discussed. Numerical examples are given for the purpose of illustration.
基金Supported by National Natural Science Foundation of China(Grant No.10901044)Qianjiang Rencai Program of Zhejiang Province(Grant No.2010R10101)+1 种基金Scientific Research Foundation for the Returned Overseas Chinese Scholars,State Education MinistryProgram for Excellent Young Teachers in Hangzhou Normal University
文摘In this paper, we introduce a type of approximation operators of neural networks with sigmodal functions on compact intervals, and obtain the pointwise and uniform estimates of the ap- proximation. To improve the approximation rate, we further introduce a type of combinations of neurM networks. Moreover, we show that the derivatives of functions can also be simultaneously approximated by the derivatives of the combinations. We also apply our method to construct approximation operators of neural networks with sigmodal functions on infinite intervals.
基金This studywas supported by King Saud University,Deanship of Scientific Research,College of Engineering Research Center.
文摘The COVID-19 pandemic with its new variants has severely affected the whole world socially and economically.This study presents a novel data analysis approach to predict the spread of COVID-19.SIR and logistic models are commonly used to determine the duration at the end of the pandemic.Results show that these well-known models may provide unrealistic predictions for countries that have pandemics spread with multiple peaks and waves.A new prediction approach based on the sigmoidal transition(ST)model provided better estimates than the traditional models.In this study,a multiple-term sigmoidal transition(MTST)model was developed and validated for several countries with multiple peaks and waves.This approach proved to fit the actual data better and allowed the spread of the pandemic to be accurately tracked.The UK,Italy,Saudi Arabia,and Tunisia,which experienced several peaks of COVID-19,were used as case studies.The MTST model was validated for these countries for the data of more than 500 days.The results show that the correlating model provided good fits with regression coefficients(R2)>0.999.The estimated model parameters were obtained with narrow 95%confidence interval bounds.It has been found that the optimum number of terms to be used in the MTST model corresponds to the highest R^(2),the least RMSE,and the narrowest 95%confidence interval having positive bounds.
基金supported by the National Natural Science Foundation of China(Grant Nos.42474054,42030112)the National Key Research and Development Program Project(Grant No.2021YFC3000500)+3 种基金the Science and Technology Innovation Program of Hunan Province(Grant No.2023SK2012)the Nature Science Foundation of Hunan Province(Grant No.2024JJ6411)the Research Foundation of Education Bureau of Hunan Province(Grant No.23C0295)the Nature Science Foundation of Shaoyang City(Grant No.2024PT6099).
文摘Reservoir landslides are significant geological hazards that pose severe risks to reservoir safety.Detecting the spatial-temporal evolution of slope movement is crucial for effective risk assessment and disaster mitigation.InSAR technology has been extensively employed to monitor surface deformations in reservoir landslides.However,the accuracy of InSAR-derived deformation fields is often limited by the reliability of prior deformation model.Traditional models,which primarily rely on linear or periodic function,frequently overlook the step-like evolution characteristics of reservoir landslides.To address this limitation,this study introduces a multi-temporal InSAR approach that incorporates Sigmoid function to enhance the deformation modeling of reservoir landslides.To solve the nonlinear parameters within the model,Taylor series expansion-based observation equation is constructed to estimate these parameters accurately.The proposed model was evaluated using both the simulated and real datasets from the Hongyanzi landslide in the Pubugou reservoir area.The results demonstrate that the proposed model significantly improves the accuracies of parameter estimation and deformation time-series.Experiments conducted under the sensitivity of interferogram stacks and varying atmospheric phase screen interference magnitudes further confirm the proposed model’s robustness and application potential.In addition,the sensitivity analysis of the initial parameters in the real data experiment scenario demonstrates the robustness of the proposed model’s nonlinear parameter estimation.Finally,the cross-correlation analysis reveals that the deformation of the Hongyanzi landslide is triggered by the decline of the reservoir water level,and quantitatively evaluates the lag time between the deformation and the reservoir water level.Our results offer novel insights for InSAR monitoring of other complex deformation evolution scenarios.Prior information is incorporated into the deformation modeling to estimate a more reliable InSAR deformation field.
文摘构造一种适用于反向传播(backpropagation,BP)神经网络的新型激活函数Lfun(logarithmic series function),并使用基于该函数的BP神经网络进行机床能耗状态的预测。首先,分析Sigmoid系列和ReLU系列激活函数的特点和缺陷,结合对数函数,构造了一种非线性分段含参数激活函数。该函数可导且光滑、导数形式简单、单调递增、输出均值为零,且通过可变参数使函数形式更灵活;其次,通过数值仿真实验在公共数据集上将Lfun函数与Sigmoid、ReLU、tanh、Leaky_ReLU和ELU函数的性能进行对比;最后,使用基于Lfun函数的BP神经网络进行机床能耗状态的预测。实验结果表明,使用Lfun函数的BP神经网络相较于使用其他几种常用激活函数的网络具有更好的性能。
