I reflect upon the development of nonlinear time series analysis since 1990 by focusing on five major areas of development. These areas include the interface between nonlinear time series analysis and chaos, the nonpa...I reflect upon the development of nonlinear time series analysis since 1990 by focusing on five major areas of development. These areas include the interface between nonlinear time series analysis and chaos, the nonparametric/semiparametric approach, nonlinear state space modelling, financial time series and nonlinear modelling of panels of time series.展开更多
Cadmium and its compounds are currently known as Class I carcinogens,and excessive intake can cause severe health damage to humans.Rice has a strong absorption effect on cadmium,and rice products with excessive cadmiu...Cadmium and its compounds are currently known as Class I carcinogens,and excessive intake can cause severe health damage to humans.Rice has a strong absorption effect on cadmium,and rice products with excessive cadmium content have caused several significant public health contamination incidents.It is essential to predict the development trend of cadmium hazards in the rice supply chain so that countermeasures can be formulated to reduce the hazards.This paper proposes a deep prediction model for cadmium hazards in the rice supply chain based on the regularization method.Firstly,a long and short-term memory network is used to build the depth prediction model by using the regularization method,and the noise penalty term is added to reduce the model fitting to the noise and prevent the over-fitting caused by the noise.Finally,the optimization of the model hyperparameters was carried out using a Bayesian optimization approach to develop the prediction performance.Then,early warning system for prediction of cadmium hazards in the rice supply chain is built based on the deep prediction model proposed in this paper with SOA architecture,including data resource,business logic,and application service layers.The proposed model performs well on an actual data set of cadmium hazards in the rice supply chain and fits the data well.展开更多
基金Supported by Biological & Biotechnology Research Council and the Engineering & Physical Science Research Council of the United Kingdom,and by the Research Grant Council of Hong Kong.
文摘I reflect upon the development of nonlinear time series analysis since 1990 by focusing on five major areas of development. These areas include the interface between nonlinear time series analysis and chaos, the nonparametric/semiparametric approach, nonlinear state space modelling, financial time series and nonlinear modelling of panels of time series.
基金supported in part by the National Key Research and Development Program of China(2017YFC1600605,2020YFC1606801).
文摘Cadmium and its compounds are currently known as Class I carcinogens,and excessive intake can cause severe health damage to humans.Rice has a strong absorption effect on cadmium,and rice products with excessive cadmium content have caused several significant public health contamination incidents.It is essential to predict the development trend of cadmium hazards in the rice supply chain so that countermeasures can be formulated to reduce the hazards.This paper proposes a deep prediction model for cadmium hazards in the rice supply chain based on the regularization method.Firstly,a long and short-term memory network is used to build the depth prediction model by using the regularization method,and the noise penalty term is added to reduce the model fitting to the noise and prevent the over-fitting caused by the noise.Finally,the optimization of the model hyperparameters was carried out using a Bayesian optimization approach to develop the prediction performance.Then,early warning system for prediction of cadmium hazards in the rice supply chain is built based on the deep prediction model proposed in this paper with SOA architecture,including data resource,business logic,and application service layers.The proposed model performs well on an actual data set of cadmium hazards in the rice supply chain and fits the data well.
