This research work investigates the use of Artificial Neural Network (ANN) based on models for solving first and second order linear constant coefficient ordinary differential equations with initial conditions. In par...This research work investigates the use of Artificial Neural Network (ANN) based on models for solving first and second order linear constant coefficient ordinary differential equations with initial conditions. In particular, we employ a feed-forward Multilayer Perceptron Neural Network (MLPNN), but bypass the standard back-propagation algorithm for updating the intrinsic weights. A trial solution of the differential equation is written as a sum of two parts. The first part satisfies the initial or boundary conditions and contains no adjustable parameters. The second part involves a feed-forward neural network to be trained to satisfy the differential equation. Numerous works have appeared in recent times regarding the solution of differential equations using ANN, however majority of these employed a single hidden layer perceptron model, incorporating a back-propagation algorithm for weight updation. For the homogeneous case, we assume a solution in exponential form and compute a polynomial approximation using statistical regression. From here we pick the unknown coefficients as the weights from input layer to hidden layer of the associated neural network trial solution. To get the weights from hidden layer to the output layer, we form algebraic equations incorporating the default sign of the differential equations. We then apply the Gaussian Radial Basis function (GRBF) approximation model to achieve our objective. The weights obtained in this manner need not be adjusted. We proceed to develop a Neural Network algorithm using MathCAD software, which enables us to slightly adjust the intrinsic biases. We compare the convergence and the accuracy of our results with analytic solutions, as well as well-known numerical methods and obtain satisfactory results for our example ODE problems.展开更多
Based on some language testing theories, an analysis of an English Mid-term Examination of grade eight students of Jingzhi Middle School is made in this paper. By means of SPSS （Statistical Package for the Social Sci...Based on some language testing theories, an analysis of an English Mid-term Examination of grade eight students of Jingzhi Middle School is made in this paper. By means of SPSS （Statistical Package for the Social Sciences） statistical software, the paper makes a whole analysis of the test paper, which covers descriptive statistics, reliability, and validity. The study aims to find some problems of the test paper, teachers＇ teaching, and students＇ learning. Thus, based on the results of analysis, this paper aims to improve the quality of test papers and give some advice to language teaching展开更多
Chinese traditional shadow play has been selected into the List of Intangible Cultural Heritage in 2011.Yet,reflecting abundant national cultural values,such traditional art form is degenerating and fading out from pe...Chinese traditional shadow play has been selected into the List of Intangible Cultural Heritage in 2011.Yet,reflecting abundant national cultural values,such traditional art form is degenerating and fading out from people’s sight.As the earliest statistical analysis software,Statistical Package for the Social Science(SPSS)is comprehensive in analyzing and managing statistical data.This study explores the application of SPSS in minimizing the workload of researchers while raising the validity of data in supporting the analysis of the survey data which reflected the inheritance and development of Chinese traditional shadow play in schools.展开更多
The traditional intrusion detection system has the problem of high false positive rate and false negative rate.This paper deeply analyzes the differences of statistical features between single-flow and multi-flow on t...The traditional intrusion detection system has the problem of high false positive rate and false negative rate.This paper deeply analyzes the differences of statistical features between single-flow and multi-flow on the database network,and presents a group of features that are easy to acquire and can be used to detect the anomaly in database network efficiently.By applying this group of features in Fisher algorithm for anomaly detection,the false positive rate and false negative rate are dramatically reduced.Simultaneously,the model made by using the group of features has the advantages of low algorithm complexity,good detection result and strong generalization ability.Experimental results show that there is higher accuracy when using the features of single-flow and multiflow to construct the anomaly detection model than only using single-flow features.展开更多
文摘This research work investigates the use of Artificial Neural Network (ANN) based on models for solving first and second order linear constant coefficient ordinary differential equations with initial conditions. In particular, we employ a feed-forward Multilayer Perceptron Neural Network (MLPNN), but bypass the standard back-propagation algorithm for updating the intrinsic weights. A trial solution of the differential equation is written as a sum of two parts. The first part satisfies the initial or boundary conditions and contains no adjustable parameters. The second part involves a feed-forward neural network to be trained to satisfy the differential equation. Numerous works have appeared in recent times regarding the solution of differential equations using ANN, however majority of these employed a single hidden layer perceptron model, incorporating a back-propagation algorithm for weight updation. For the homogeneous case, we assume a solution in exponential form and compute a polynomial approximation using statistical regression. From here we pick the unknown coefficients as the weights from input layer to hidden layer of the associated neural network trial solution. To get the weights from hidden layer to the output layer, we form algebraic equations incorporating the default sign of the differential equations. We then apply the Gaussian Radial Basis function (GRBF) approximation model to achieve our objective. The weights obtained in this manner need not be adjusted. We proceed to develop a Neural Network algorithm using MathCAD software, which enables us to slightly adjust the intrinsic biases. We compare the convergence and the accuracy of our results with analytic solutions, as well as well-known numerical methods and obtain satisfactory results for our example ODE problems.
文摘Based on some language testing theories, an analysis of an English Mid-term Examination of grade eight students of Jingzhi Middle School is made in this paper. By means of SPSS （Statistical Package for the Social Sciences） statistical software, the paper makes a whole analysis of the test paper, which covers descriptive statistics, reliability, and validity. The study aims to find some problems of the test paper, teachers＇ teaching, and students＇ learning. Thus, based on the results of analysis, this paper aims to improve the quality of test papers and give some advice to language teaching
文摘Chinese traditional shadow play has been selected into the List of Intangible Cultural Heritage in 2011.Yet,reflecting abundant national cultural values,such traditional art form is degenerating and fading out from people’s sight.As the earliest statistical analysis software,Statistical Package for the Social Science(SPSS)is comprehensive in analyzing and managing statistical data.This study explores the application of SPSS in minimizing the workload of researchers while raising the validity of data in supporting the analysis of the survey data which reflected the inheritance and development of Chinese traditional shadow play in schools.
基金supported by the Key Project in the National Science and Technology Pillar Program (No.2008BAH37B04)the 111 Project (No.B08004).
文摘The traditional intrusion detection system has the problem of high false positive rate and false negative rate.This paper deeply analyzes the differences of statistical features between single-flow and multi-flow on the database network,and presents a group of features that are easy to acquire and can be used to detect the anomaly in database network efficiently.By applying this group of features in Fisher algorithm for anomaly detection,the false positive rate and false negative rate are dramatically reduced.Simultaneously,the model made by using the group of features has the advantages of low algorithm complexity,good detection result and strong generalization ability.Experimental results show that there is higher accuracy when using the features of single-flow and multiflow to construct the anomaly detection model than only using single-flow features.
