Tian Fu-peng

Bio: Tian Fu-peng is an academic researcher from Northwest University for Nationalities. The author has contributed to research in topics: Activation function & Artificial neural network. The author has an hindex of 3, co-authored 5 publications receiving 22 citations.

Research of Hypertension Morbidity Situation in Haixizhou Region Based on ADL Model

Abstract: Based on the theory of ADL model,the relationship between mean pressure and hypertension morbidity each month is studied by establishing the ADL model.In this paper,EViews software is used to perform statistical analysis for the registration data of the hypertension cases monitored for Qinghai region in Haixizhou,at the same time the ADL model is established by using the original data.The rationality of the ADL model established is conformed by performing correlation test.

Research of cerebral hemorrhage morbidity situation in Haixizhou region based on PDL Model

Abstract: Based on the theory of PDL model, through the established different PDL models to study the relationship between mean pressure and cerebral hemorrhage morbidity each month. EViews software for the Qinghai region in Haixizhou cerebral hemorrhage made to monitor cases of the registration data for statistical analysis, at the same time using the originate number established different PDL models. Through comparative analysis, selected the optimization PDL model among them. When arrived the certain lag period, the influence between mean pressure and cerebral hemorrhage morbidity approached to zero.

Research of Nephritis Morbidity Situation in Haixizhou Region Based on Season Model

Abstract: Based on the theory of season model,through the established different mathematic models,the number of nephritis cases each month is studied.In this paper,EViews software for the Qinghai region in Haixizhou nephritis is used to monitor cases of the registration data for statistical analysis,at the same time the adjustment method is used to adjust the original number,eliminating seasonal fluctuation and interferon of season,and different mathematic models are applied to predict the original number.The development trend of nephritis' number each month is observed.

Pneumonia Incidence Rate Predictive Model of Nonlinear Time Series Based on Dynamic Learning Rate BP Neural Network

Abstract: Objective to explore predictive method of nonlinear time series based on using BP neural network. Methods Based on dynamic learning rate BP artificial neural network with Hyperbolic Tangent function as activation function has been used. Results Build two kinds of ANN forecast models of pneumonia incidence rate. They are better than traditional method on prediction precision. Conclusion BP artificial neural network can be used to forecast for disease incidence rate.

Application study on BP neural network in the incidence rate of pulmonary emphysema

Abstract: The connotation of BP neural network algorithm and code was introduced. The construction of expert system for forecasting the incidence rate of pulmonary emphysema based on BP neural network was discussed. Methods The data of incidence rate of pulmonary emphysema and meteorological factors in plateau section from 2003 to 2009 were collected and analyzed by using Eviews for windows, version 3.1; the model of Back Propagation artificial neural network was built by Matlab, version7.0. The MER and R2 of pulmonary emphysema incidence rate forecasting model were 0.60401% and 0.95329 respectively. The effect of fitting and forecasting of the model were very well. BP neural network had a strong application value in the forecasting the incidence rate of pulmonary emphysema.

Pneumonia Incidence Rate Predictive Model of Nonlinear Time Series Based on Dynamic Learning Rate BP Neural Network

Abstract: Objective to explore predictive method of nonlinear time series based on using BP neural network. Methods Based on dynamic learning rate BP artificial neural network with Hyperbolic Tangent function as activation function has been used. Results Build two kinds of ANN forecast models of pneumonia incidence rate. They are better than traditional method on prediction precision. Conclusion BP artificial neural network can be used to forecast for disease incidence rate.

Application study on BP neural network in the incidence rate of pulmonary emphysema

Abstract: The connotation of BP neural network algorithm and code was introduced. The construction of expert system for forecasting the incidence rate of pulmonary emphysema based on BP neural network was discussed. Methods The data of incidence rate of pulmonary emphysema and meteorological factors in plateau section from 2003 to 2009 were collected and analyzed by using Eviews for windows, version 3.1; the model of Back Propagation artificial neural network was built by Matlab, version7.0. The MER and R2 of pulmonary emphysema incidence rate forecasting model were 0.60401% and 0.95329 respectively. The effect of fitting and forecasting of the model were very well. BP neural network had a strong application value in the forecasting the incidence rate of pulmonary emphysema.

A disease forecasting algorithm based on single factor correlation analysis and the JacUOD algorithm

Abstract: There is a close relationship between the occurrence of a variety of diseases and meteorological factors. However, the typical disease forecasting methods are based on history data and the requirement of initial data is strict. To solve these problems, we proposed a disease forecasting algorithm to adapt to real-time data. The proposed algorithm has two contributions: (1) It uses the single factor correlation analysis methods when selecting meteorological factors that affect disease (2) It introduces a new method to calculate disease prediction to build date _number _meteorological factor matrix and use JacUOD algorithm to evaluate the similarity of meteorological factors between the target dates and past ones. To find out the top-N dates are of the maximum similarity with the target one, therefore, we could forecast the number combining the similarity value and the N date's patient number. Obviously, the number of patient is obtained by calculating the similarity of different dates' meteorological factors. Experiments show that the algorithm generates a better accuracy than the traditional algorithms in disease prediction.

Applying Rough Set Theory to Establish Artificial Neural Networks Model for Short Term Incidence Rate Forecasting

Abstract: Choosing input variable and networks architecture are key processes for modeling short term incidence rate forecast by artificial neural networks, in this paper a method based on rough set theory is proposed to deal with them. In the proposed approach, the key factors that affect the incidence rate forecasting are firstly identified by rough set theory and then the input variables of forecast model can be determined. On the basis of the process mentioned above a set of influence rules can been obtained through reductive mining process of attributes and attribute values, then a neural networks of incidence rate forecast model is established on the rule set and BP-algorithm is adopt to optimize the networks. The method indicates that incidence rate forecast model can be established according some theoretical principles and avoiding blindness. A practical application is given at last to demonstrate the usefulness of the novel method.

Forecast of Cerebral Infraction Incidence Rate Based on BP Neural Network and ARIMA Combined Model

Abstract: Objective Forecast and analysis of cerebral infraction incidence rate are the basis and key work of cerebral infraction prevention and control. At present, forecast of cerebral infraction incidence rate is mainly based on traditional research approach or single artificial neural network technology. Recent study results show that combined forecast model approach enjoys more precise forecast than monomial forecast approach. Methods The paper proposes a new forecast approach based on BP neural network and ARIMA combined mode and makes comprehensive analysis and forecast of the changing trend of cerebral infraction incidence rate in Haixizhou region, Qinghai province of China. Results Forecast results indicate that this approach is more precise in terms of monomial forecast method. Conclusion The combined model is feasible and effective in the forecast of cerebral infraction incidence rate.