The amino acid composition and the biased auto-correlation function are considered as features, BP neural network algorithm is used to synthesize these features. The prediction accuracy of this method is verified by u...The amino acid composition and the biased auto-correlation function are considered as features, BP neural network algorithm is used to synthesize these features. The prediction accuracy of this method is verified by using the independent non-homologous protein database. It is shown that the average absolute errors for resubstitution test are 0.070 and 0.068 with the standard deviations 0.049 and 0.047 for the prediction of the content of α-helix and β-sheet respectively. For cross-validation test, the average absolute errors are 0.075 and 0.070 with the standard deviations 0.050 and 0.049 for the prediction of the content of α-helix and β-sheet respectively. Compared with the other methods currently available, the BP neural network method combined with the amino acid composition and the biased auto-correlation function features can effectively improve the prediction accuracy.展开更多
In this paper, we theoretically analyze the transformation of marketing strategy and the countermeasures under the network economic times. Network marketing is based on the network technology, including the whole proc...In this paper, we theoretically analyze the transformation of marketing strategy and the countermeasures under the network economic times. Network marketing is based on the network technology, including the whole process of marketing activities as a new form of marketing. In this paper, we analyze the issue from the listed perspectives. （1） Ultra space-and-time. The traditional marketing has very strong boundedness to the region, and this boundedness displays, the specifi c transaction can only be closed in the specific region, if the enterprise wants to expand the market share, only then establishes the retailing organization. （2） Interactivity. Network as a media, with the one-on-one interaction, this feature allows companies to basic communicate with consumers, and strengthen consumer participation, to determine the product form, function and the price. （3） The symmetry of information. Consumers can fi nd all kinds of that related products on the Internet information, there are plenty of time to judge the various products and prices. Companies will also be released in a timely manner on the Internet the company’s latest product information. Under this basis, we propose the new idea on the network marketing development direction that will help to build up the more efficient marketing system.展开更多
In this paper, a back propagation artificial neural network （BP-ANN） model is presented for the simultaneous estimation of vapour liquid equilibria （VLE） of four binary systems viz chlorodifluoromethan-carbondioxi...In this paper, a back propagation artificial neural network （BP-ANN） model is presented for the simultaneous estimation of vapour liquid equilibria （VLE） of four binary systems viz chlorodifluoromethan-carbondioxide, trifluoromethan-carbondioxide, carbondisulfied-trifluoromethan and carbondisulfied-chlorodifluoromethan. VLE data of the systems were taken from the literature for wide ranges of temperature （222.04-343.23K） and pressure （0.105 to 7.46MPa）. BP-ANN trained by the Levenberg-Marquardt algorithm in the MATLAB neural network toolbox was used for building and optimizing the model. It is shown that the established model could estimate the VLE with satisfactory precision and accuracy for the four systems with the root mean square error in the range of 0.054-0.119. Predictions using BP-ANN were compared with the conventional Redlich-Kwang-Soave （RKS） equation of state, suggesting that BP-ANN has better ability in estimation as compared with the RKS equation （the root mean square error in the range of 0.115-0.1546）.展开更多
Due to the limited cognition of meridian structure, the study of the essence of meridian phenomenon is faced with obstruction. This paper aims to expound on the multi-substantial structures of meridians and put forwar...Due to the limited cognition of meridian structure, the study of the essence of meridian phenomenon is faced with obstruction. This paper aims to expound on the multi-substantial structures of meridians and put forward hypothesis on multi-substantial structures of meridians based on embryology.展开更多
As a marine disaster,red tides have a serious impact on marine fisheries,ecology,economy,human production and life.Red tides have been widely concerned by researchers for a long time.However,due to its complex formati...As a marine disaster,red tides have a serious impact on marine fisheries,ecology,economy,human production and life.Red tides have been widely concerned by researchers for a long time.However,due to its complex formation mechanism,red tide forecasting is extremely challenging.Aiming at addressing problem of red tide forecasting,this paper collects the marine monitoring data before and after the occurrence of red tide in Xiamen sea area,and analyzes the correlation between multiple environmental factors and the red tide occurrence by combining the methods of Pearson correlation coefficient,Scatter matrix,and multiple correlation coefficient.The fusion method of LSTM and CNN based on deep learning are applied to mine the temporal dependence of environmental factors and find the local features of sequence data,then predict the occurrence of red tides.In the Xiamen No.1 and Xiamen No.2 datasets,the RMSE and MAE errors of this method are reaching 0.5218 and 0.5043,respectively.The forecast probability of red tide occurrence was further determined through the collaborative comparison model.The final forecast accuracy of the two datasets is 67.58%and 63.49%,respectively.This study provides exploratory experience for the analysis and forecasting of red tides,which proves the feasibility of applying deep learning methods to red tide forecasting.展开更多
Accurate prediction of refrigerant boiling heat transfer coefficients is important for the design of evaporators. The generalized correlations have different forms, and could not provide satisfactory results for R22 a...Accurate prediction of refrigerant boiling heat transfer coefficients is important for the design of evaporators. The generalized correlations have different forms, and could not provide satisfactory results for R22 and its alternative refrigerants R134a, R407C and R410A. This study proposes to use artificial neural network (ANNs) as a generalized correlation model, selects the input parameters of ANNs on the basis of the dimensionless parameter groups of existing correlations, and correlates the in-tube boiling heat transfer coefficients of the above four refrigerants. The results show that the ANNs model with the input and output based on the Liu-Winterton correlation has the best result. The root-mean-square deviations in training and test are 15.5% and 20.2% respectively, and approximately 85% of the deviations are within ±20%, which is much better than that of the existing generalized correlations.展开更多
An incremental time-delay neural network based on synapse growth, which is suitable for dynamic control and learning of autonomous robots, is proposed to improve the learning and retrieving performance of dynamical re...An incremental time-delay neural network based on synapse growth, which is suitable for dynamic control and learning of autonomous robots, is proposed to improve the learning and retrieving performance of dynamical recurrent associative memory architecture. The model allows steady and continuous establishment of associative memory for spatio-temporal regularities and time series in discrete sequence of inputs. The inserted hidden units can be taken as the long-term memories that expand the capacity of network and sometimes may fade away under certain condition. Preliminary experiment has shown that this incremental network may be a promising approach to endow autonomous robots with the ability of adapting to new data without destroying the learned patterns. The system also benefits from its potential chaos character for emergence.展开更多
Due to the limitation of Edlen Equation to compensate for air refractivity in ordinary air pressure, an experiment to study the relationship between air refractivity and temperature, along with its pressure, is design...Due to the limitation of Edlen Equation to compensate for air refractivity in ordinary air pressure, an experiment to study the relationship between air refractivity and temperature, along with its pressure, is designed and carried out from ordinary pressure to low pressure. The expansion of Edlen Equation is achieved by using the cascade-Correlation learning method, and a neural network architecture model. The applied accuracy of neural network is the same as that of Edlen Equation in an ordinary pressure zone.展开更多
There is still an obstacle to prevent neural network from wider and more effective applications, i.e., the lack of effective theories of models identification. Based on information theory and its generalization, this ...There is still an obstacle to prevent neural network from wider and more effective applications, i.e., the lack of effective theories of models identification. Based on information theory and its generalization, this paper introduces a universal method to achieve nonlinear models identification. Two key quantities, which are called nonlinear irreducible auto-correlation (NIAC) and generalized nonlinear irreducible auto-correlation (GNIAC), are defined and discussed. NIAC and GNIAC correspond with intrinstic irreducible auto-(dependency) (IAD) and generalized irreducible auto-(dependency) (GIAD) of time series respectively. By investigating the evolving trend of NIAC and GNIAC, the optimal auto-regressive order of nonlinear auto-regressive models could be determined naturally. Subsequently, an efficient algorithm computing NIAC and GNIAC is discussed. Experiments on simulating data sets and typical nonlinear prediction models indicate remarkable correlation between optimal auto-regressive order and the highest order that NIAC-GNIAC have a remarkable non-zero value, therefore demonstrate the validity of the proposal in this paper.展开更多
The authors discussed the method of wavelet neural network （WNN） for correlation of base-level cycle. A new vectored method of well log data was proposed. Through the training with the known data set, the WNN can re...The authors discussed the method of wavelet neural network （WNN） for correlation of base-level cycle. A new vectored method of well log data was proposed. Through the training with the known data set, the WNN can remenber the cycle pattern characteristic of the well log curves. By the trained WNN to identify the cycle pattern in the vectored log data, the ocrrdation process among the well cycles was completed. The application indicates that it is highly efficient and reliable in base-level cycle correlation.展开更多
A comprehensive quantification method of fatigue degree is proposed concerning subjective and objective quantifications.Using the fatigue degree test software,fatigue degree is objectively quantified by analyzing the ...A comprehensive quantification method of fatigue degree is proposed concerning subjective and objective quantifications.Using the fatigue degree test software,fatigue degree is objectively quantified by analyzing the reaction and operation abilities of drivers about traffic signals.By comparison experiment with that EEG signal based,multivariate statistical analysis and fusion identification based on BP neural network(BPNN) results show that the experimental procedure is simple and practical,and the proposed method can reveal the correlation between fatigue feature parameters and fatigue degree in theory,and also can achieve accurate and reliable quantification of fatigue degree,especially under the associated action of multiple fatigue feature parameters.展开更多
文摘The amino acid composition and the biased auto-correlation function are considered as features, BP neural network algorithm is used to synthesize these features. The prediction accuracy of this method is verified by using the independent non-homologous protein database. It is shown that the average absolute errors for resubstitution test are 0.070 and 0.068 with the standard deviations 0.049 and 0.047 for the prediction of the content of α-helix and β-sheet respectively. For cross-validation test, the average absolute errors are 0.075 and 0.070 with the standard deviations 0.050 and 0.049 for the prediction of the content of α-helix and β-sheet respectively. Compared with the other methods currently available, the BP neural network method combined with the amino acid composition and the biased auto-correlation function features can effectively improve the prediction accuracy.
文摘In this paper, we theoretically analyze the transformation of marketing strategy and the countermeasures under the network economic times. Network marketing is based on the network technology, including the whole process of marketing activities as a new form of marketing. In this paper, we analyze the issue from the listed perspectives. （1） Ultra space-and-time. The traditional marketing has very strong boundedness to the region, and this boundedness displays, the specifi c transaction can only be closed in the specific region, if the enterprise wants to expand the market share, only then establishes the retailing organization. （2） Interactivity. Network as a media, with the one-on-one interaction, this feature allows companies to basic communicate with consumers, and strengthen consumer participation, to determine the product form, function and the price. （3） The symmetry of information. Consumers can fi nd all kinds of that related products on the Internet information, there are plenty of time to judge the various products and prices. Companies will also be released in a timely manner on the Internet the company’s latest product information. Under this basis, we propose the new idea on the network marketing development direction that will help to build up the more efficient marketing system.
文摘In this paper, a back propagation artificial neural network （BP-ANN） model is presented for the simultaneous estimation of vapour liquid equilibria （VLE） of four binary systems viz chlorodifluoromethan-carbondioxide, trifluoromethan-carbondioxide, carbondisulfied-trifluoromethan and carbondisulfied-chlorodifluoromethan. VLE data of the systems were taken from the literature for wide ranges of temperature （222.04-343.23K） and pressure （0.105 to 7.46MPa）. BP-ANN trained by the Levenberg-Marquardt algorithm in the MATLAB neural network toolbox was used for building and optimizing the model. It is shown that the established model could estimate the VLE with satisfactory precision and accuracy for the four systems with the root mean square error in the range of 0.054-0.119. Predictions using BP-ANN were compared with the conventional Redlich-Kwang-Soave （RKS） equation of state, suggesting that BP-ANN has better ability in estimation as compared with the RKS equation （the root mean square error in the range of 0.115-0.1546）.
文摘Due to the limited cognition of meridian structure, the study of the essence of meridian phenomenon is faced with obstruction. This paper aims to expound on the multi-substantial structures of meridians and put forward hypothesis on multi-substantial structures of meridians based on embryology.
文摘As a marine disaster,red tides have a serious impact on marine fisheries,ecology,economy,human production and life.Red tides have been widely concerned by researchers for a long time.However,due to its complex formation mechanism,red tide forecasting is extremely challenging.Aiming at addressing problem of red tide forecasting,this paper collects the marine monitoring data before and after the occurrence of red tide in Xiamen sea area,and analyzes the correlation between multiple environmental factors and the red tide occurrence by combining the methods of Pearson correlation coefficient,Scatter matrix,and multiple correlation coefficient.The fusion method of LSTM and CNN based on deep learning are applied to mine the temporal dependence of environmental factors and find the local features of sequence data,then predict the occurrence of red tides.In the Xiamen No.1 and Xiamen No.2 datasets,the RMSE and MAE errors of this method are reaching 0.5218 and 0.5043,respectively.The forecast probability of red tide occurrence was further determined through the collaborative comparison model.The final forecast accuracy of the two datasets is 67.58%and 63.49%,respectively.This study provides exploratory experience for the analysis and forecasting of red tides,which proves the feasibility of applying deep learning methods to red tide forecasting.
文摘Accurate prediction of refrigerant boiling heat transfer coefficients is important for the design of evaporators. The generalized correlations have different forms, and could not provide satisfactory results for R22 and its alternative refrigerants R134a, R407C and R410A. This study proposes to use artificial neural network (ANNs) as a generalized correlation model, selects the input parameters of ANNs on the basis of the dimensionless parameter groups of existing correlations, and correlates the in-tube boiling heat transfer coefficients of the above four refrigerants. The results show that the ANNs model with the input and output based on the Liu-Winterton correlation has the best result. The root-mean-square deviations in training and test are 15.5% and 20.2% respectively, and approximately 85% of the deviations are within ±20%, which is much better than that of the existing generalized correlations.
文摘An incremental time-delay neural network based on synapse growth, which is suitable for dynamic control and learning of autonomous robots, is proposed to improve the learning and retrieving performance of dynamical recurrent associative memory architecture. The model allows steady and continuous establishment of associative memory for spatio-temporal regularities and time series in discrete sequence of inputs. The inserted hidden units can be taken as the long-term memories that expand the capacity of network and sometimes may fade away under certain condition. Preliminary experiment has shown that this incremental network may be a promising approach to endow autonomous robots with the ability of adapting to new data without destroying the learned patterns. The system also benefits from its potential chaos character for emergence.
文摘Due to the limitation of Edlen Equation to compensate for air refractivity in ordinary air pressure, an experiment to study the relationship between air refractivity and temperature, along with its pressure, is designed and carried out from ordinary pressure to low pressure. The expansion of Edlen Equation is achieved by using the cascade-Correlation learning method, and a neural network architecture model. The applied accuracy of neural network is the same as that of Edlen Equation in an ordinary pressure zone.
文摘There is still an obstacle to prevent neural network from wider and more effective applications, i.e., the lack of effective theories of models identification. Based on information theory and its generalization, this paper introduces a universal method to achieve nonlinear models identification. Two key quantities, which are called nonlinear irreducible auto-correlation (NIAC) and generalized nonlinear irreducible auto-correlation (GNIAC), are defined and discussed. NIAC and GNIAC correspond with intrinstic irreducible auto-(dependency) (IAD) and generalized irreducible auto-(dependency) (GIAD) of time series respectively. By investigating the evolving trend of NIAC and GNIAC, the optimal auto-regressive order of nonlinear auto-regressive models could be determined naturally. Subsequently, an efficient algorithm computing NIAC and GNIAC is discussed. Experiments on simulating data sets and typical nonlinear prediction models indicate remarkable correlation between optimal auto-regressive order and the highest order that NIAC-GNIAC have a remarkable non-zero value, therefore demonstrate the validity of the proposal in this paper.
基金Supported by Project of Dagang Branch of Petroleum Group Company Ltd,CNPC No TJDG-JZHT-2005-JSDW-0000-00339
文摘The authors discussed the method of wavelet neural network （WNN） for correlation of base-level cycle. A new vectored method of well log data was proposed. Through the training with the known data set, the WNN can remenber the cycle pattern characteristic of the well log curves. By the trained WNN to identify the cycle pattern in the vectored log data, the ocrrdation process among the well cycles was completed. The application indicates that it is highly efficient and reliable in base-level cycle correlation.
基金Supported by the National Nature Science Foundation of China(No.61304205,61203273,61103086,41301037)the Open Funding Project of State Key Laboratory of Virtual Reality Technology and Systems,Beihang University(No.BUAA-VR-13KF-04)+1 种基金Jiangsu Ordinary University Science Research Project(No.13KJB120007)Innovation and Entrepreneurship Training Project of College Students(No.201410300153,201410300165)
文摘A comprehensive quantification method of fatigue degree is proposed concerning subjective and objective quantifications.Using the fatigue degree test software,fatigue degree is objectively quantified by analyzing the reaction and operation abilities of drivers about traffic signals.By comparison experiment with that EEG signal based,multivariate statistical analysis and fusion identification based on BP neural network(BPNN) results show that the experimental procedure is simple and practical,and the proposed method can reveal the correlation between fatigue feature parameters and fatigue degree in theory,and also can achieve accurate and reliable quantification of fatigue degree,especially under the associated action of multiple fatigue feature parameters.
