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
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
Determining the liquefaction potential of soil is important in earthquake engineering. This study proposes the use of the Relevance Vector Machine (RVM) to determine the liquefaction potential of soil by using actual ...Determining the liquefaction potential of soil is important in earthquake engineering. This study proposes the use of the Relevance Vector Machine (RVM) to determine the liquefaction potential of soil by using actual cone penetration test (CPT) data. RVM is based on a Bayesian formulation of a linear model with an appropriate prior that results in a sparse representation. The results are compared with a widely used artifi cial neural network (ANN) model. Overall, the RVM shows good performance and is proven to be more accurate than the ANN model. It also provides probabilistic output. The model provides a viable tool for earthquake engineers to assess seismic conditions for sites that are susceptible to liquefaction.展开更多
To understand the complexity of the mathematical models of a proton exchange membrane fuel cell (PEMFC) and their shortage of practical PEMFC control, the PEMFC complex mechanism and the existing PEMFC models are anal...To understand the complexity of the mathematical models of a proton exchange membrane fuel cell (PEMFC) and their shortage of practical PEMFC control, the PEMFC complex mechanism and the existing PEMFC models are analyzed, and artificial neural networks based PEMFC modeling is advanced. The structure, algorithm, training and simulation of PEMFC modeling based on improved BP networks are given out in detail. The computer simulation and conducted experiment verify that this model is fast and accurate, and can be used as a suitable operational model for PEMFC real-time control.展开更多
Geometrical optimization and electrostatic potential calculations have been per- formed for a series of halogenated hydrocarbons at the HF/ Gen-6d level. A number of electrostatic potentials and the statistically base...Geometrical optimization and electrostatic potential calculations have been per- formed for a series of halogenated hydrocarbons at the HF/ Gen-6d level. A number of electrostatic potentials and the statistically based structural descriptors derived from these electrostatic potentials have been obtained. Multiple linear regression analysis and artificial neural network are employed simultaneously in this paper. The result shows that the parameters derived from electrostatic potentials σ 2tot, V s and ∑ V s+, together with the molecular volume (Vmc) can be used to ex- press the quantitative structure-infinite dilution activity coefficients (γ∞) relationship of halogenated hydrocarbons in water. The result also demonstrates that the model obtained by using BFGS quasi- Newton neural network method has much better predictive capability than that from multiple linear regression. The goodness of the model has been validated through exploring the predictive power for the external test set. The model obtained via neural network may be applied to predict γ∞ of other halogenated hydrocarbons not present in the data set.展开更多
In order to catch more process details in chemical processes,a dynamic model for prediction of process trends is proposed by modifying traditional time-series ANN (artificial neural networks) model with impules respon...In order to catch more process details in chemical processes,a dynamic model for prediction of process trends is proposed by modifying traditional time-series ANN (artificial neural networks) model with impules response indentification means.The application result of the model is briefly discussed.展开更多
A preliminary estimation of ablation property for carbon-carbon composites by artificial neutral net (ANN) method was presented.It was found that the carbon-carbon composites' density,degree of graphitization and ...A preliminary estimation of ablation property for carbon-carbon composites by artificial neutral net (ANN) method was presented.It was found that the carbon-carbon composites' density,degree of graphitization and the sort of matrix are the key controlling factors for its ablative performance.Then,a brief fuzzy mathe- matical relationship was established between these factors and ablative performance.Through experiments, the performance of the ANN was evaluated,which was used in the ablative performance prediction of C/C composites.When the training set,the structure and the training parameter of the net change,the best match ratio of these parameters was achieved.Based on the match ratio,this paper forecasts and evalu- ates the carbon-carbon ablation performance.Through experiences,the ablative performance prediction of carbon-carbon using ANN can achieve the line ablation rate,which satisfies the need of precision of practical engineering fields.展开更多
To eliminate harmonic pollution incurred from the static synchronous compensator(STATCOM), a method of applying artificial neural network is presented. When PWM wave is formed based on the harmonic suppression theory,...To eliminate harmonic pollution incurred from the static synchronous compensator(STATCOM), a method of applying artificial neural network is presented. When PWM wave is formed based on the harmonic suppression theory, a concave is set on certain angle of the square wave to suppress unnecessary harmonics, by timely and on-line determining the chopping angle corresponding to respective harmonics through artificial neural network, i.e. by setting the position of concave to eliminate corresponding harmonics, the harmonic component on output voltage of the inverter can be improved. To conclude through computer simulation test, the perfect control effect has been proved.展开更多
Considering the non-linear,complex and multivariable process of biological denitrification,an activated sludge process was introduced to remove nitrate in groundwater with the aid of artificial neural networks (ANN) t...Considering the non-linear,complex and multivariable process of biological denitrification,an activated sludge process was introduced to remove nitrate in groundwater with the aid of artificial neural networks (ANN) to evaluate the nitrate removal effect. The parameters such as COD,NH3-N,NO-3-N,NO-2-N,MLSS,DO,etc.,were used for input nodes,and COD,NH3-N,NO-3-N,NO-2-N were selected for output nodes. Experimental ANN training results show that ANN was able to predict the output water quality parameters very well. Most of relative errors of NO-3-N and COD were in the range of ±10% and ±5% respectively. The results predicted by ANN model of nitrate removal in groundwater produced good agreement with the experimental data. Though ANN model can optimize effect of the whole system,it cannot replace the water treatment process.展开更多
This paper presented an online quality inspection system based on artificial neural networks. Chromatism classification and edge detection are two difficult problems in glass steel surface quality inspection. Two arti...This paper presented an online quality inspection system based on artificial neural networks. Chromatism classification and edge detection are two difficult problems in glass steel surface quality inspection. Two artificial neural networks were made and the two problems were solved. The one solved chromatism classification. Hue, saturation and their probability of three colors, whose appearing probabilities were maximum in color histogram, were selected as input parameters, and the number of output node could be adjusted with the change of requirement. The other solved edge detection. In this neutral network, edge detection of gray scale image was able to be tested with trained neural networks for a binary image. It prevent the difficulty that the number of needed training samples was too large if gray scale images were directly regarded as training samples. This system is able to be applied to not only glass steel fault inspection but also other product online quality inspection and classification.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
Since the complexity and structural diversity of man-made compounds are considered, quantitative structure-activity relationships (QSARs)-based fast screening approaches are urgently needed for the assessment of the p...Since the complexity and structural diversity of man-made compounds are considered, quantitative structure-activity relationships (QSARs)-based fast screening approaches are urgently needed for the assessment of the potential risk of endocrine disrupting chemicals (EDCs). The artificial neural net-works (ANN) are capable of recognizing highly nonlinear relationships, so it will have a bright applica-tion prospect in building high-quality QSAR models. As a popular supervised training algorithm in ANN, back-propagation (BP) converges slowly and immerses in vibration frequently. In this paper, a research strategy that BP neural network was improved by conjugate gradient (CG) algorithm with a variable selection method based on genetic algorithm was applied to investigate the QSAR of EDCs. This re-sulted in a robust and highly predictive ANN model with R2 of 0.845 for the training set, q2pred of 0.81 and root-mean-square error (RMSE) of 0.688 for the test set. The result shows that our method can provide a feasible and practical tool for the rapid screening of the estrogen activity of organic compounds.展开更多
Based on the six months data set of ARGO-YBJ experiment with analog read-out and its Monte Carlo simulation, we study the difference between different primaries induced showers by using the space-time information of t...Based on the six months data set of ARGO-YBJ experiment with analog read-out and its Monte Carlo simulation, we study the difference between different primaries induced showers by using the space-time information of the charged particles in Extensive Air Showers. With five parameters which can effciently pick out primary proton induced showers as inputs of an artificial neural network, the proton spectrum from 100 TeV to 10 PeV can be obtained.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.
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Determining the liquefaction potential of soil is important in earthquake engineering. This study proposes the use of the Relevance Vector Machine (RVM) to determine the liquefaction potential of soil by using actual cone penetration test (CPT) data. RVM is based on a Bayesian formulation of a linear model with an appropriate prior that results in a sparse representation. The results are compared with a widely used artifi cial neural network (ANN) model. Overall, the RVM shows good performance and is proven to be more accurate than the ANN model. It also provides probabilistic output. The model provides a viable tool for earthquake engineers to assess seismic conditions for sites that are susceptible to liquefaction.
文摘To understand the complexity of the mathematical models of a proton exchange membrane fuel cell (PEMFC) and their shortage of practical PEMFC control, the PEMFC complex mechanism and the existing PEMFC models are analyzed, and artificial neural networks based PEMFC modeling is advanced. The structure, algorithm, training and simulation of PEMFC modeling based on improved BP networks are given out in detail. The computer simulation and conducted experiment verify that this model is fast and accurate, and can be used as a suitable operational model for PEMFC real-time control.
文摘Geometrical optimization and electrostatic potential calculations have been per- formed for a series of halogenated hydrocarbons at the HF/ Gen-6d level. A number of electrostatic potentials and the statistically based structural descriptors derived from these electrostatic potentials have been obtained. Multiple linear regression analysis and artificial neural network are employed simultaneously in this paper. The result shows that the parameters derived from electrostatic potentials σ 2tot, V s and ∑ V s+, together with the molecular volume (Vmc) can be used to ex- press the quantitative structure-infinite dilution activity coefficients (γ∞) relationship of halogenated hydrocarbons in water. The result also demonstrates that the model obtained by using BFGS quasi- Newton neural network method has much better predictive capability than that from multiple linear regression. The goodness of the model has been validated through exploring the predictive power for the external test set. The model obtained via neural network may be applied to predict γ∞ of other halogenated hydrocarbons not present in the data set.
文摘In order to catch more process details in chemical processes,a dynamic model for prediction of process trends is proposed by modifying traditional time-series ANN (artificial neural networks) model with impules response indentification means.The application result of the model is briefly discussed.
基金supported by the National Natural Science Foundation of China under Grant No.10572044.
文摘A preliminary estimation of ablation property for carbon-carbon composites by artificial neutral net (ANN) method was presented.It was found that the carbon-carbon composites' density,degree of graphitization and the sort of matrix are the key controlling factors for its ablative performance.Then,a brief fuzzy mathe- matical relationship was established between these factors and ablative performance.Through experiments, the performance of the ANN was evaluated,which was used in the ablative performance prediction of C/C composites.When the training set,the structure and the training parameter of the net change,the best match ratio of these parameters was achieved.Based on the match ratio,this paper forecasts and evalu- ates the carbon-carbon ablation performance.Through experiences,the ablative performance prediction of carbon-carbon using ANN can achieve the line ablation rate,which satisfies the need of precision of practical engineering fields.
文摘To eliminate harmonic pollution incurred from the static synchronous compensator(STATCOM), a method of applying artificial neural network is presented. When PWM wave is formed based on the harmonic suppression theory, a concave is set on certain angle of the square wave to suppress unnecessary harmonics, by timely and on-line determining the chopping angle corresponding to respective harmonics through artificial neural network, i.e. by setting the position of concave to eliminate corresponding harmonics, the harmonic component on output voltage of the inverter can be improved. To conclude through computer simulation test, the perfect control effect has been proved.
基金National Hi-Tech Research and Development Program of China (Grant No.863-2003AA601120).
文摘Considering the non-linear,complex and multivariable process of biological denitrification,an activated sludge process was introduced to remove nitrate in groundwater with the aid of artificial neural networks (ANN) to evaluate the nitrate removal effect. The parameters such as COD,NH3-N,NO-3-N,NO-2-N,MLSS,DO,etc.,were used for input nodes,and COD,NH3-N,NO-3-N,NO-2-N were selected for output nodes. Experimental ANN training results show that ANN was able to predict the output water quality parameters very well. Most of relative errors of NO-3-N and COD were in the range of ±10% and ±5% respectively. The results predicted by ANN model of nitrate removal in groundwater produced good agreement with the experimental data. Though ANN model can optimize effect of the whole system,it cannot replace the water treatment process.
基金Supported by Science and Technology Fundation (China University of Geosciences) (No.200520)
文摘This paper presented an online quality inspection system based on artificial neural networks. Chromatism classification and edge detection are two difficult problems in glass steel surface quality inspection. Two artificial neural networks were made and the two problems were solved. The one solved chromatism classification. Hue, saturation and their probability of three colors, whose appearing probabilities were maximum in color histogram, were selected as input parameters, and the number of output node could be adjusted with the change of requirement. The other solved edge detection. In this neutral network, edge detection of gray scale image was able to be tested with trained neural networks for a binary image. It prevent the difficulty that the number of needed training samples was too large if gray scale images were directly regarded as training samples. This system is able to be applied to not only glass steel fault inspection but also other product online quality inspection and classification.
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the National Natural Science Foundation of China (Grant No. 20507008)the National Natural Science Foundation Key Project of China (Grant No. 20737001)+1 种基金the Natural Science Foundation of Jiangsu Province,China (Grant No. BK200418)the National Basic Research Program of China (973 Program) (Grant No. 2003CB415002)
文摘Since the complexity and structural diversity of man-made compounds are considered, quantitative structure-activity relationships (QSARs)-based fast screening approaches are urgently needed for the assessment of the potential risk of endocrine disrupting chemicals (EDCs). The artificial neural net-works (ANN) are capable of recognizing highly nonlinear relationships, so it will have a bright applica-tion prospect in building high-quality QSAR models. As a popular supervised training algorithm in ANN, back-propagation (BP) converges slowly and immerses in vibration frequently. In this paper, a research strategy that BP neural network was improved by conjugate gradient (CG) algorithm with a variable selection method based on genetic algorithm was applied to investigate the QSAR of EDCs. This re-sulted in a robust and highly predictive ANN model with R2 of 0.845 for the training set, q2pred of 0.81 and root-mean-square error (RMSE) of 0.688 for the test set. The result shows that our method can provide a feasible and practical tool for the rapid screening of the estrogen activity of organic compounds.
基金National Natural Science Foundation of China (10120130794)
文摘Based on the six months data set of ARGO-YBJ experiment with analog read-out and its Monte Carlo simulation, we study the difference between different primaries induced showers by using the space-time information of the charged particles in Extensive Air Showers. With five parameters which can effciently pick out primary proton induced showers as inputs of an artificial neural network, the proton spectrum from 100 TeV to 10 PeV can be obtained.
