AIM: To study the application of artificial neural network (ANN) in forecasting the incidence of hepatitis A, which had an autoregression phenomenon.METHODS: The data of the incidence of hepatitis A in Liaoning Provin...AIM: To study the application of artificial neural network (ANN) in forecasting the incidence of hepatitis A, which had an autoregression phenomenon.METHODS: The data of the incidence of hepatitis A in Liaoning Province from 1981 to 2001 were obtained from Liaoning Disease Control and Prevention Center. We used the autoregressive integrated moving average (ARIMA) model of time series analysis to determine whether there was any autoregression phenomenon in the data. Then the data of the incidence were switched into [0,1] intervals as the network theoretical output. The data from 1981 to 1997 were used as the training and veriying sets and the data from 1998 to 2001 were made up into the test set.STATISTICA neural network (ST NN) was used to construct,train and simulate the artificial neural network.RESULTS: Twenty-four networks were tested and seven were retained. The best network we found had excellent performance, its regression ratio was 0.73, and its correlation was 0.69. There were 2 input variables in the network, one was AR(1), and the other was time. The number of units in hidden layer was 3. In ARIMA time series analysis results, the best model was first order autoregression without difference and smoothness. The total sum square error of the ANN model was 9 090.21, the sum square error of the training set and testing set was 8 377.52 and 712.69,respectively, they were all less than that of ARIMA model.The corresponding value of ARIMA was 12 291.79, 8 944.95 and 3 346.84, respectively. The correlation coefficient of nonlinear regression (RNL) of ANN was 0.71, while the RNL of ARIMA linear autoregression model was 0.66.CONCLUSION: ANN is superior to conventional methods in forecasting the incidence of hepatitis A which has an autoregression phenomenon.展开更多
Asynchronous motor overturn with a vectorial control system，Developments on bearingless drive technology，Identifying the asynchronous motor inner values，On-line estimation of quantities using artificial neural netw...Asynchronous motor overturn with a vectorial control system，Developments on bearingless drive technology，Identifying the asynchronous motor inner values，On-line estimation of quantities using artificial neural networks，Research on flywheel energy storage system for power quality，SIMULATION OF A PV PANEL-INVERTERMOTOPUMP ASSOCIATION IN PHOTOVOLTAIC PUMPING SYSTEMS，Simulation of field orientation control for a two-phase asynchronous motor。展开更多
A novel paradigm for telemedicine using the personal bio-monitor,Computer tomography based diagnosis using extended logic programming and artificial neural networks.Estimation of relevant data for a SVM-classification...A novel paradigm for telemedicine using the personal bio-monitor,Computer tomography based diagnosis using extended logic programming and artificial neural networks.Estimation of relevant data for a SVM-classification.Evaluating an intelligent diagnosis system of historical text comprehension.Fault intelligent diagnosis for high-pressure feed-water heater system of a 300 MW coal-fired power unit based on improved BP neural network.展开更多
AIM: To investigate the role of artifi cial neural networks in predicting the presence of thyroid disease in atrophic body gastritis patients. METHODS: A dataset of 29 input variables of 253 atrophic body gastritis pa...AIM: To investigate the role of artifi cial neural networks in predicting the presence of thyroid disease in atrophic body gastritis patients. METHODS: A dataset of 29 input variables of 253 atrophic body gastritis patients was applied to artifi cial neural networks (ANNs) using a data optimisation procedure (standard ANNs,T&T-IS protocol,TWIST protocol). The target variable was the presence of thyroid disease. RESULTS: Standard ANNs obtained a mean accuracy of 64.4% with a sensitivity of 69% and a specifi city of 59.8% in recognizing atrophic body gastritis patients with thyroid disease. The optimization procedures (T&T-IS and TWIST protocol) improved the performance of the recognition task yielding a mean accuracy,sensitivity and specifi city of 74.7% and 75.8%,78.8% and 81.8%,and 70.5% and 69.9%,respectively. The increase of sensitivity of the TWIST protocol was statistically signifi cant compared to T&T-IS. CONCLUSION: This study suggests that artificial neural networks may be taken into consideration as a potential clinical decision-support tool for identifying ABG patients at risk for harbouring an unknown thyroid disease and thus requiring diagnostic work-up of their thyroid status.展开更多
In order to catch more process details in chemical processes, adynamic model for prediction of process trends is proposed bymodifying traditional time-series ANN (artificial neural networks)model with impulse response...In order to catch more process details in chemical processes, adynamic model for prediction of process trends is proposed bymodifying traditional time-series ANN (artificial neural networks)model with impulse response identification means. The applicationresults of the model is briefly discussed.展开更多
An artificial neural network is used to predict the performance of fabrics in clothing manufacturing. The predictions are based on fabric mechanical properties measured on the FAST system. The influences of the differ...An artificial neural network is used to predict the performance of fabrics in clothing manufacturing. The predictions are based on fabric mechanical properties measured on the FAST system. The influences of the different ANNs construct on the convergence speed and the prediction accuracy are investigated. The result indicates that the BP neural network is an efficiency technique and has a wide prospect in the application to garment processing.展开更多
基金Supported by the National Natural Science Foundation of China,No.30170833
文摘AIM: To study the application of artificial neural network (ANN) in forecasting the incidence of hepatitis A, which had an autoregression phenomenon.METHODS: The data of the incidence of hepatitis A in Liaoning Province from 1981 to 2001 were obtained from Liaoning Disease Control and Prevention Center. We used the autoregressive integrated moving average (ARIMA) model of time series analysis to determine whether there was any autoregression phenomenon in the data. Then the data of the incidence were switched into [0,1] intervals as the network theoretical output. The data from 1981 to 1997 were used as the training and veriying sets and the data from 1998 to 2001 were made up into the test set.STATISTICA neural network (ST NN) was used to construct,train and simulate the artificial neural network.RESULTS: Twenty-four networks were tested and seven were retained. The best network we found had excellent performance, its regression ratio was 0.73, and its correlation was 0.69. There were 2 input variables in the network, one was AR(1), and the other was time. The number of units in hidden layer was 3. In ARIMA time series analysis results, the best model was first order autoregression without difference and smoothness. The total sum square error of the ANN model was 9 090.21, the sum square error of the training set and testing set was 8 377.52 and 712.69,respectively, they were all less than that of ARIMA model.The corresponding value of ARIMA was 12 291.79, 8 944.95 and 3 346.84, respectively. The correlation coefficient of nonlinear regression (RNL) of ANN was 0.71, while the RNL of ARIMA linear autoregression model was 0.66.CONCLUSION: ANN is superior to conventional methods in forecasting the incidence of hepatitis A which has an autoregression phenomenon.
文摘Asynchronous motor overturn with a vectorial control system，Developments on bearingless drive technology，Identifying the asynchronous motor inner values，On-line estimation of quantities using artificial neural networks，Research on flywheel energy storage system for power quality，SIMULATION OF A PV PANEL-INVERTERMOTOPUMP ASSOCIATION IN PHOTOVOLTAIC PUMPING SYSTEMS，Simulation of field orientation control for a two-phase asynchronous motor。
文摘A novel paradigm for telemedicine using the personal bio-monitor,Computer tomography based diagnosis using extended logic programming and artificial neural networks.Estimation of relevant data for a SVM-classification.Evaluating an intelligent diagnosis system of historical text comprehension.Fault intelligent diagnosis for high-pressure feed-water heater system of a 300 MW coal-fired power unit based on improved BP neural network.
基金funds from MIUR 2005 (Italian Ministry for University and Research) and University Sapienza Roma
文摘AIM: To investigate the role of artifi cial neural networks in predicting the presence of thyroid disease in atrophic body gastritis patients. METHODS: A dataset of 29 input variables of 253 atrophic body gastritis patients was applied to artifi cial neural networks (ANNs) using a data optimisation procedure (standard ANNs,T&T-IS protocol,TWIST protocol). The target variable was the presence of thyroid disease. RESULTS: Standard ANNs obtained a mean accuracy of 64.4% with a sensitivity of 69% and a specifi city of 59.8% in recognizing atrophic body gastritis patients with thyroid disease. The optimization procedures (T&T-IS and TWIST protocol) improved the performance of the recognition task yielding a mean accuracy,sensitivity and specifi city of 74.7% and 75.8%,78.8% and 81.8%,and 70.5% and 69.9%,respectively. The increase of sensitivity of the TWIST protocol was statistically signifi cant compared to T&T-IS. CONCLUSION: This study suggests that artificial neural networks may be taken into consideration as a potential clinical decision-support tool for identifying ABG patients at risk for harbouring an unknown thyroid disease and thus requiring diagnostic work-up of their thyroid status.
文摘In order to catch more process details in chemical processes, adynamic model for prediction of process trends is proposed bymodifying traditional time-series ANN (artificial neural networks)model with impulse response identification means. The applicationresults of the model is briefly discussed.
文摘An artificial neural network is used to predict the performance of fabrics in clothing manufacturing. The predictions are based on fabric mechanical properties measured on the FAST system. The influences of the different ANNs construct on the convergence speed and the prediction accuracy are investigated. The result indicates that the BP neural network is an efficiency technique and has a wide prospect in the application to garment processing.
