Application Of Back Propagation Neural Network Research Articles

Application of BPNN’s intelligent ETQ model in network English teaching

Teaching evaluation is a key initiative to improve the quality of education and teaching. The research significance of this study is rooted in addressing the limitations of the traditional evaluation of teaching quality (ETQ) model, which often relies on a single evaluation index, exhibits a one-sided perspective, and suffers from pronounced subjectivity. In this context, this paper delves into the application of the backpropagation neural network (BPNN) to enhance and refine the ETQ model. The intelligent ETQ model was constructed and utilized in network English teaching to enhance the effect and quality of network English teaching. By analyzing the characteristics and needs of network English teaching, the advantages of BPNN in the ETQ were explored. The intelligent evaluation model was constructed, and its application effect in network English teaching was studied and evaluated. The total number of students satisfied with the BPNN based network English ETQ model was 151, with a total satisfaction rate of 75.5%. The ETQ model on the basis of BPNN was applied to network English teaching, which helped the average final score of Class 2 improve by 5.44 points compared to the division exam. The ETQ model based on BPNN was applied to network English teaching, which can improve the rationality of teaching evaluation and help improve students’ school English proficiency.

Application of BP Neural Network and Fixed Effect in the Relationship between CSR and Female Directors

This study is to illustrate the two-way influencing mechanism of female directors’ gender and corporate social responsibility (CSR) of China’s listed companies. According to an empirical test of 3723 enterprises from 2013 to 2020, it reveals that the presence of female directors—especially female executive directors—contributes to the improvement of CSR. In the stage of back propagation (BP) neural network, the model included the overalls of CSR and macroscopic strategic, fulfillment content, and disclosed technology three dimensions. It randomly selects 100 samples, and via BP neural network, it predicts that the RKS-CSR rating can effectively show that high-CSR lever enhances the presence of female directors in Chinese listed companies and promotes gender balance in directors’ team, thus achieving gender balance and CSR two-way sustainable development.

Research on Handwritten Chinese Character Recognition Based on BP Neural Network

The application of pattern recognition technology enables us to solve various human-computer interaction problems that were difficult to solve before. Handwritten Chinese character recognition, as a hot research object in image pattern recognition, has many applications in people’s daily life, and more and more scholars are beginning to study off-line handwritten Chinese character recognition. This paper mainly studies the recognition of handwritten Chinese characters by BP (Back Propagation) neural network. Establish a handwritten Chinese character recognition model based on BP neural network, and then verify the accuracy and feasibility of the neural network through GUI (Graphical User Interface) model established by Matlab. This paper mainly includes the following aspects: Firstly, the preprocessing process of handwritten Chinese character recognition in this paper is analyzed. Among them, image preprocessing mainly includes six processes: graying, binarization, smoothing and denoising, character segmentation, histogram equalization and normalization. Secondly, through the comparative selection of feature extraction methods for handwritten Chinese characters, and through the comparative analysis of the results of three different feature extraction methods, the most suitable feature extraction method for this paper is found. Finally, it is the application of BP neural network in handwritten Chinese character recognition. The establishment, training process and parameter selection of BP neural network are described in detail. The simulation software platform chosen in this paper is Matlab, and the sample images are used to train BP neural network to verify the feasibility of Chinese character recognition. Design the GUI interface of human-computer interaction based on Matlab, show the process and results of handwritten Chinese character recognition, and analyze the experimental results.

Application of BP neural network to prediction of recovery effect of air-foam flooding in heavy oil

Air-foam flooding is an effective technology to enhance oil recovery in heavy oil production. In this study, Back Propagation (BP) neural network model is built and optimized to predict the recovery effect of heavy oil with the injection parameters data of the oxygen-reduced air-foam flooding obtained through experiments. The study shows that the prediction results of the BP neural network is quite accurate, and the average absolute relative error of prediction results is less than 0.21%. This also illustrates the feasibility of using the BP network prediction model in predicting heavy oil recovery effect.

Modeling of lead removal by living Scenedesmus obliquus using backpropagation (BP) neural network algorithm

Abstract Lead pollution in aquatic environment possessed lethal threats to public health. Heavy metal removal using microalgae have gained increasing attention as a novel biosorbent with great economic potential. However, excessive time consumption has been a limiting factor in algal adsorption experiments, while artificial neural network (ANN) prediction models could significantly improve the efficiency for experimental research. In this study, backpropagation (BP) neural network model was established for Pb 2 + removal from water by Scenedesmus obliquus with the 4:9:1 (input layer–hidden layer–output layer) construction. The experimental data of 275 groups derived from orthogonal experiments were obtained for training, validation and test data as input layer. Tangent sigmoid transfer function (tansig) was used in the hidden layer, while linear transfer function was used on the output layer (purelin). The correlation coefficient R2 reached 0.997 for the entire data set after training. Testing model results showed that the correlation between the predicted data and the experimental data was 0.997, with an accuracy rate of 95.4%, coefficient of determination of 0.999, root mean square error of 0.226, relative error of 1.47% and P value of 0.004. The predicted data of BP neural network also successfully applied in the pseudo-second-order kinetic model and Langmuir thermal dynamic model with the relative error less than 5%. Results showed that under pH 6 and algal biomass 2 g/L, Scenedesmus Obliquus achieved highest Pb 2 + removal efficiency. The successful application of BP neural network in Pb 2 + removal by Scenedesmus Obliquus provided an alternative and a more efficient method for microalgal adsorption experimental design and data validation.

Adoption of Back Propagation Network Improved by Particle Swarm Optimization in Network Intrusion Detection

In order to scientifically and reasonably solve some problems in the existing intrusion detection system and effectively maintain the security of network information, the improved particle swarm optimization (PSO) algorithm and back propagation (BP) neural network are used to form a new algorithm to optimize the intrusion detection technology. First, the traditional PSO algorithm is improved, and it is used to optimize the BP neural network. Then the fused new algorithm is adopted to establish an intrusion detection model. Finally, the detection effect of the new model is verified through the experiment. The results show that the detection rate of the intrusion detection model for network attacks can be as high as 93.31%, and it also greatly reduces the false positive rate of the system, which can be as low as 1.85% and has a high practical application value. This study has important reference value for the application of BP neural network improved by PSO in network security.

Estimation of construction project building cost by back-propagation neural network

PurposeBuilding cost is an important part of construction projects, and its correct estimation has important guiding significance for the follow-up decision-making of construction units.Design/methodology/approachThis study focused on the application of back-propagation (BP) neural network in the estimation of building cost. First, the influencing factors of building cost were analyzed. Six factors were selected as input of the estimation model. Then, a BP neural network estimation model was established and trained by ten samples.FindingsAccording to the experimental results, it was found that the estimation model converged at about 85 times; compared with radial basis function (RBF), the estimation accuracy of the model was higher, and the average error was 5.54 per cent, showing a good reliability in cost estimation.Originality/valueThe results of this study provide a reliable basis for investment decision-making in the construction industry and also contribute to the further application of BP neural network in cost estimation.

Application of BP neural network to the prediction of coal ash melting characteristic temperature

The characteristic temperature of coal ash melting strongly influences the blast furnace injection process. The coal ash deformation temperature (DT) is determined by its chemical composition, but relationship between the two remains uncertain. In this paper, the traditional linear regression, Factsage calculation, and back-propagation (BP) neural network calculation are used to predict the coal ash deformation temperature. The results show that the melting characteristic temperature of coal ash has a great relationship with the coal ash composition. The linear regression can predict the change trend of coal ash deformation temperature, but the prediction results are not very satisfactory. The calculation results of Factsage show a great deviation from the experimental values. The prediction results of the BP neural network can achieve good accuracy, and the maximum relative average error of prediction results is 6.67%. This also illustrates the feasibility of using the BP network prediction model in predicting coal ash deformation temperature.

Application of BP Neural Network Based on Genetic Algorithm Optimization in Evaluation of Power Grid Investment Risk

The artificial intelligence calculation method can effectively solve various nonlinear mapping relationships. The strength of these nonlinear solvers is exploited for the evaluation of power grid investment risk using back propagation (BP) neural network optimized by genetic algorithm. The mathematical model of the problem is constructed by selecting the transfer function of the neural network and defining the fitness function of genetic algorithm. BP neural network has good ability of self-learning, self-adaptation and generalization, which can overcome the drawbacks of traditional evaluation methods relying on experts' experience. For the characteristics of genetic algorithm global optimization, the genetic algorithm is used to optimize the weight and threshold of BP neural network, and BP neural network is trained to obtain the optimal evaluation model. The model fully exploits the local search ability of BP neural network and the global search ability of genetic algorithm. It has obtained good evaluation accuracy for the processing of multi-dimensional influence factor problem. And the model can be adapted to different power grids by changing the training data. However, the method cannot describe the specific relationship between each impact factor and the investment risk of the grid. The case study shows that the method can accurately and effectively evaluate power grid investment risk and improve the fault tolerance of the power grid investment risk evaluation.

Design and analysis of a general data evaluation system based on social networks

A social network is a social structure with a set of social actors and other social interactions between actors. The social network supports a set of methods to explain the structure of whole social entities as well as a variety of theories in these structures. The study of these structures uses social network analysis to explain local and global patterns and examine network dynamics. The related economic data is important for our economic developments. We could analyze the economic data to explore the investments. In this paper, we focus on the design and analysis of automatic management systems to make a decision about the petroleum projects.Petroleum is the lifeblood of economic development. So, we need an automatic and effective management system to make the mining investments of petroleum. Before deciding to invest in the petroleum project, the engineers will describe this project by providing the sufficient economic data. Based on this data, the decision can undertake professional analysis, to determine whether the project is feasible. To automate the manual process and overcome the traditional evaluation method, the BP (back propagation) neural network is applied in a petroleum project economic evaluation. This method opens up a new way for the evaluation of petroleum projects. In this article, we first introduce the application of BP neural network. Then, we introduce the principle and shortcoming of BP neural network. Based on it, we optimize the BP neural network. Secondly, according to the characteristics of petroleum projects, we obtain the economic evaluation indicators about petroleum projects, which are used as the input of the BP neural network. Thirdly, we propose the design of the BP neural network and select the smallest simulation error of the BP neural network as a forecasting model of petroleum projects. In order to improve the accuracy, the labeled training samples are selected for BP neural network training. Finally, based on extensive simulation experimental results, we demonstrate our scheme about the BP neural network of petroleum project evaluation is effective and has overperformance than traditional schemes in terms of convenience and accurate decision-making suggestions.

A constrained optimization method based on BP neural network

A constrained optimization method based on back-propagation (BP) neural network is proposed in this paper. Taking the maximization of output for example, using unipolar sigmoid function as transfer function, the method presents a general mathematical expression of BP neural network constrained optimization and derives the partial derivative of output with respect to input. On this basis, the fundamental idea, algorithms and related models are given in this article. When BP neural network is on the basis of fitting, this method can adjust the input values of BP neural network to make the output values maximal or minimal. Therefore, with this method the application of BP neural network is expanded by combining BP network’s fitting with optimization. At the same time, the article also provides a new method to study the black-box problem. The experiments show that the constrained optimization method is effective.

Detection and Recognition of Selected Class Railway Signs

Abstract The paper aims at presentation of results of research on detection and recognition of selected class railway signs (W11p). When conducting the research, the authors have proposed their own algorithm, which achieved about 90% effectiveness at detecting W11p signs and 98% effectiveness at classifying them. The processes of localisation, segmentation and recognition of W11p signs were considerably simplified thanks to the application of backpropagation neural network. The authors believe that two non-standard methods related to the use of the network deserve attention: the application of an interactive method of generating the training set, owing to which also pixels highly diversified in terms of their colours could be included, and the use of a full spectrum of neural network responses, which made it possible to accomplish a feedback. It consisted in an automatic adjusting of the network responses’ threshold to the results of segmentation and recognition.

Application of back-propagation neural network on bank destruction forecasting for accumulative landslides in the three Gorges Reservoir Region, China

In recent years, a large number of bank destruction occur in the reservoir area under the effect of water fluctuation, which may be lead to reservoir accumulative landslide geological hazards finally. The paper conducted the bank destruction forecasting study for accumulative landslides in the Three Gorges Reservoir Region, China utilizing back-propagation (BP) neural network approach. A representative scenario of Jinle landslide is then taken for analysis purposes. On the basis of the existing data sets of bank destruction cases, the BP neural network forecasting model and the corresponding programs for bank destruction are both presented, whose forecasting result is validated by two independent approaches, namely empirical method and numerical modeling method. Furthermore, the BP neural network model had obvious advantages over the convention approaches in the aspects of the fast calculation speed and high convenience. According to the bank destruction forecasting scale presented above, the corresponding revetment measures can be proposed to prevent the occurring of the bank destruction, whose effectiveness has been further validated by the actual engineering practice.

Diagnostics of Induction Motor Based on Spectral Analysis of Stator Current with Application of Backpropagation Neural Network

Nowadays PC computers make possible the signal characteristics by calculation. The paper presents an automatic computerized system for the diagnosis of the rotor bars of the induction motor by applying spectral analysis and backpropagation neural network. Software to recognize the current of induction motor was implemented. System of current recognition is based on a study of the frequency spectrum of stator current. The studies were conducted for two conditions of induction motor. The results of the numerical experiments are presented and discussed in the paper. The researches show that the system can be useful for protection of the engines and metallurgical equipment.

Application of back-propagation neural network for controlling the front end bending phenomenon in plate rolling

In this paper, a new method of controlling the front end bending in plate rolling is introduced. In this method, the back-propagation neural network with three layers is designed. The input layer has three inputs of temperature, entry thickness, and parameter of deformation zone. The hidden layer has seven neurons. The only value of the output layer is the front end bending value. The optimised rolling schedule at different conditions is obtained after training and calculating. Its application to a plate rolling mill shows that the method solves the problem of the front end bending successfully.

Short-term power load forecasting based on IVL-BP neural network technology

Accurate forecasting of power load has been one of the important issues in the electricity industry. Recently, along with the privatization and the deregulation, accurate forecasting of power load draws more and more attentions. There are many difficulties in the application of BP neural network which is a very useful tool for the forecasting, such as the defining for the network structure and the local solution which is easy to fall into. To solve these problems, the back-propagation (BP) neural network short-term load forecasting method based on improved variable learning rate back propagation (IVL-BP) is presented in this paper. Though introducing two threshold parameters for the amount of the mean square increasing and decreasing, the learning algorithm is sensitive to the error and convergence speed. Then use genetic algorithm to train network parameters until the error tending to some stable value. Then conduct BP algorithm with the optimized weights to achieve short-term load forecasting. The experimental results have shown that the load forecasting system based on this method has higher accuracy and real-time.

Application of backpropagation neural network for spindle vibration-based tool wear monitoring in micro-milling

This study develops a micro-tool condition monitoring system consisting of accelerometers on the spindle, a data acquisition and signal transformation module, and a backpropagation neural network. This study also discusses the effect of the sensor installations, selected features, and the bandwidth size of the features on the classification rate. To collect the vibration signals necessary for training the system model and verifying the system, an experiment was implemented on a micro-milling research platform along with a 700 μm diameter micro-end mill and a SK2 workpiece. A three-axis accelerometer was installed on a sensor plate attached to the spindle housing to collect vibration signals in three directions during cutting. The frequency domain features representing changes in tool wear were selected based on the class mean scatter criteria after transforming signals from the time domain to the frequency domain by fast Fourier transform. Using the appropriate vibration features, this study develops and tests a backpropagation neural network classifier. Results show that proper feature extraction for classification provides a better solution than applying all spectral features into the classifier. Selecting five features for classification provides a better classification rate than the case with four and three features along with the 30 Hz bandwidth size of the spectral feature. Moreover, combining the signals for tool condition from both direction signals provides a better classification rate than determining the tool condition using a one-direction single sensor.

Application of Back-propagation Neural Network to Categorization of Physical Fitness Levels of Taiwanese Females

The purpose of this study is to establish the feasibility of adopting the back-propagation neural network (BPNN) to predict fitness category. In this study, 2218 healthy Taiwanese females aged 20 to 65 participated. Data collected included five parameters required for the physical fitness (PF) passport: subject's age, body mass index (BMI), performance in the sit-and-reach test, 1-min bent-leg curl-ups, and cardiorespiratory endurance. The network structure of BPNN adopted here consisted of three layers: input layer (5 neurons), hidden layer (5 neurons), and output layer (4 neurons). To prove the ability of BPNN in categorizing PF accurately and speedily, its learning effect must be confirmed. To achieve this purpose, the samples were divided randomly into two parts: training samples (n=1218) and testing samples (n=1000). Thereafter, learning algorithms of the BPNN were executed. The learning rate was assumed to be 0.75, and 1000 learning cycles were run. The results demonstrated that the root mean square (RMS) for the training samples was 0.059, while the RMS for the testing samples was 0.065. Such small RMS is evidence that the BPNN converged well and had a good learning effect. On the other hand, the mean degree of accuracy of the BPNN was 96.83% in identifying body composition, 98.41% for muscular flexibility, 94.39% for muscular strength and endurance, and 97.25% for cardiorespiratory capacity. The mean degree of accuracy for these four items was as high as 96.72%. Overall, the mean relative error for categorizing PF was 3.28%, which was within the acceptable range. Therefore, the results confirmed the reliability of the BPNN for categorizing PF. BPNN can be converted into software to assess the subject's PF in a precise and speedy manner, thus eliminating the need to refer to a chart of the modular standard to decide the fitness category.

Application of BP neural network in forecasting soil temperature time-series

Soil temperature is an important parameter in agro-meteorological observations and research on variations in soil temperature time-series provides the theoretical basis for soil solute movement and proper distribution of reference crops. Owing to the complicated characteristics and strong nonlinearity of surface soil temperature time-series in saline coastal regions, a long-term data on soil temperature was analyzed and soil temperature time-series predicted by integrated Back-Propagation (BP) neural network method and expansion of multi-dimensional time-series, using experimental data from a typical coastal region in North Jiangsu Province. The results show that BP neural network model with 7 neurons of input layer, 7 neurons of hidden layer, and 1 neuron of output layer is the best for soil temperature time-series forecast, with a minimum sum squared error of 18.017. Relative errors between forecated value and measured value of the soil temperature all fall within 0～20%, with an average relative error of only 2.94%. The high range of relative error underscores the significance of BP neural network in daily soil temperature forecasting.

An improved backpropagation neural network for detection of road-like features in satellite imagery

This paper presents an application of backpropagation neural network for the detection of linear structures in remote-sensing images. The purpose of the approach is two-fold. First, to exploit the advantages of a neural network classifier over the tranditional ones. Second, to avoid the strategic phases of enhancement and thresholding. Once the network is learnt, the classification scheme is real-time. Two critical issues in the present approach are the selection of the network architecture and the rate of convergence of learning. Solutions to these two problems are proposed. Experimental results on IRS and SPOT images are presented. Satisfactory classification results have been obtained using the network.