Improved BP Neural Network Research Articles

Forward kinematics of body posture perception using an improved BP neural network based on a quantum genetic algorithm

Forward kinematics analysis of body posture perception is the basis for studying other performance of 6-degree of freedom parallel robot. Because forward kinematics involves many sets of nonlinear equations, it is usually difficult to solve. In this paper, an improved BP neural network (BPNN) based on a quantum genetic algorithm (GA) is designed to solve the forward kinematics problem. Additionally, we use the characteristics of easy calculation of inverse kinematics to generate a dataset for training and testing. Finally, through a large number of experiments, we show that the improved strategy of BPNN by quantum GA is effective, and the accuracy of the model we designed is high enough to solve the forward kinematics of body posture perception.

Analysis and Prediction of Body Test Results Based on Improved Backpropagation Neural Network Algorithm

The mining and analysis of student achievement data is of great importance to teaching management. Using the relevant data of college students’ physical fitness test and sports performance as the object of research, a BP neural network model is developed to predict performance. Based on BP neural network, an algorithm for predicting students’ endurance performance is proposed, which is applied to the sunshine long-distance intelligent sports testing system at Hangzhou Dianzi University. The nonlinear relationship between students’ performance in sunshine running and endurance performance is determined, and students’ performance in sunshine running is used to predict their endurance performance the following year. Experimental results indicate that the accuracy of the model is above 85%. At the same time, the prediction results are combined with the Internet of things technology to produce a student sports prescription management system, which sets different sunshine running parameters for students with different predicted results and provides personalized sports prescriptions for students with different physical conditions, which has extensive and far-reaching application value.

Application of Improved BP Neural Network in the Optimization of Regional Economic Energy Consumption Structure

At present, local regions have the problems of low optimization dimension and unbalanced supply side in the energy consumption structure in the process of economic development. Based on this, this paper studies the optimization method of the regional economic energy consumption structure based on big data and BP neural network analysis strategy and designs an intelligent extraction model of the regional economic energy consumption structure based on the BP neural network. According to the correlation and internal matching of energy consumption data involved in the process of economic development in different regions, the quantitative high-value analysis of regional economic energy consumption structure is realized, and the accuracy of the analysis results is analyzed by Newton Leibniz theory. The results show that the optimization model of regional economic energy consumption structure based on the BP neural network can effectively improve the application scope and data utilization of energy consumption structure, effectively complete the intelligent classification of energy consumption of different enterprises, and indirectly improve the energy utilization and matching efficiency of the regional economy.

Research on State Trend Prediction of Hydraulic Turbine Units Based on Mult-dimensional and Multi-condition Data

Aiming at the problem that it is difficult to accurately predict the flexible operation conditions and state trend of hydraulic turbine, in this paper a cascade model of BP-LSTM classification prediction is proposed, which can identify the working conditions of existing fusion data, and then predict the measuring points of different working conditions.<BR> Based on the pressure parameters of hydraulic turbine units, the improved BP neural network is used to determine the operation conditions of hydraulic turbine units, and the classified data is redivided to establish the multivariate LSTM prediction model. By optimizing the parameters of the multivariate LSTM prediction model, such as the structure, the number of network layers and the number of hidden layer neurons, finally established the cascade model of BP-LSTM classification prediction of time series of hydraulic turbine units.<BR> Through experimental verification and analysis, BP-LSTM classification prediction model can predict the operation trend of measuring points under different working conditions after classification. Compared with other models, BP-LSTM model has higher prediction accuracy and better effect. The cascade model of BP-LSTM classification prediction of time series provides a model basis for the research of predictive control of hydraulic turbine units.

Research on Teaching Quality Evaluation Model of Higher Education Teachers Based on BP Neural Network and Random Matrix

Improving teaching quality is the first task of higher education, and evaluating teaching quality is an effective measure to improve teaching quality. Combining the advantages of BP neural network and random matrix algorithm, the teaching quality evaluation model of higher education teachers is established. In this paper, the improved BP neural network and the random matrix structure are used to normalize the indicators, evaluate the teaching indicators, and build the teacher teaching quality evaluation system model. Through experimental design, the training data set is input into the model for training. In the training process, the increase and decrease ratio of learning rate, momentum term, and other parameters are adjusted to improve the prediction accuracy and convergence speed of the model. Iteration times, training time, MSE, and prediction accuracy were taken as performance comparison indexes of the model. Experiments show that the model solves the shortcomings of the existing teaching quality evaluation methods and models to a certain extent, and improves the accuracy of evaluation prediction. When the number of iterations is 133, the prediction accuracy is as high as 94.9%, which verifies the effectiveness of the model in the evaluation of teaching quality in colleges and universities. Finally, the evaluation index system of teacher teaching quality is comprehensively analyzed, and the results prove that the evaluation model of teacher teaching quality of A university is suitable for the situation of the school, can highlight the guidance, and is scientific and measurable of evaluation.

An Improved Quality Evaluation Method for Foreign Trade English Using GA-RBF Neural Network

The teaching evaluation of foreign trade English has been in a quite difficult position because some universities have foreign trade courses, but there is no corresponding foreign trade practice base, so the evaluation of this course is currently relatively simple. This research provides an improved BP neural network evaluation method to address the issues of single teaching evaluation and poor accuracy rate of English as foreign trade in English courses. First, an improved genetic algorithm is utilized to obtain the weight factor of the neural network, which is the data input of the neural network. Second, the middle layer of the network is optimized, so that the output efficiency can be further improved. Finally, the improved and optimized neural network is simulated. The experimental simulation shows that the method proposed in this paper has an energy-efficient and objective evaluation of the quality of foreign trade English teaching with certain accuracy.

A Model of Network Security Situation Assessment Based on BPNN Optimized by SAA-SSA

In order to address the problems that the accuracy and convergence of current network security situation assessment models need to be improved, a model of network security situation assessment based on SAA-SSA-BPNN is proposed. Using the characteristics of sparrow search algorithm (SSA) optimized by simulated annealing algorithm (SAA) with good stability, fast convergence speed and is not easy to fall into local optimum to improve the BP neural network (BPNN), so as to find the best fitness individual, and obtain the optimal weight and threshold, then assign them to the BP neural network as the initial values. The preprocessed index data is input into the improved BP neural network model for training, and finally the threat degree of the network system is assessed based on the trained model. Comparative experimental results show that this assessment model has higher accuracy and faster convergence than other situation assessment models based on improved BP neural network.

Prediction of the Improvement Effect of Visualization Technology on Online Learning from the Perspective of the Neural Network

In today’s world, data visualization is employed in every aspect of life, and online course makers should take use of the wealth of behavioral data provided by students. Currently, data visualization is being used to suit the development needs of online education in the Internet age. It is also a strong assurance for the online course platform’s improvement and implementation. Data visualization is already closely related to our lives. For online education, the application of data visualization can help course builders understand learners’ learning time characteristics, learning behavior habits, and learning improvement effects, so as to provide learners with corresponding learning guidance, solve learners’ learning difficulties, and improve learning efficiency and course teaching quality. In order to confirm the improvement effect of visualization technology on online learning, the following work is done in this study. This study describes the current state of visualization technology in the United States and internationally, as well as the foundation for the prediction approach that will be proposed later. There are many factors in the evaluation of the online learning effect, and it is dynamic, which is a nonlinear manifestation. The nonlinear computing, self-learning, and high fault endurance of artificial neural network technology are used in this article, and an online learning effect improvement prediction model based on the improved BP neural network is established, namely, the Levenberg–Marquardt back propagation (LMBP) prediction model. The experimental results suggest that the model has a good level of accuracy and may be used to forecast the effect of online learning improvement.

Prediction and Planning of Sports Competition Based on Deep Neural Network.

Physical education curriculum has been paid more and more attention by teachers and parents, and having a healthy body is the foundation. School sports competition is also more and more concerned by major researchers, and scholars have produced in-depth research and analysis of sports competition results prediction because prediction results can better let teachers carry out appropriate sports training for students, so as to achieve the best learning effect. The construction of the prediction model and whether the performance and universality of the model after construction are suitable for predicting sports competitions have also become a major research point. Deep neural network is a complex network method to analyze the structure of the human brain, which plays a core role in the field of sports planning and performance prediction and can know the future performance of athletes or students in advance in sports competitions. This paper establishes the autoregressive summation model prediction model, the complex neural network prediction model, and the improved complex neural network prediction model. It is concluded that only the improved BP neural network model has a remarkable effect on performance prediction, and the prediction value obtained by this prediction model can reduce the systematic error of prediction, so that it can better infer the performance prediction of sports competitions in China and plan which sports events are suitable for which prediction model.

A BP Neural Network Algorithm for Multimedia Data Monitoring of Air Particulate Matter.

In order to study a BP neural network algorithm for air particulate matter data monitoring, firstly, the monitoring data collected by particle sensor using the light scattering method are proposed. Then, based on the improved BP neural network method, the mapping relationship between the actual measured value of the sensor, weather and other influencing factors, and the standard value of the monitoring station is established, and the calibration model of air particulate matter is realized. Finally, through theoretical analysis and experimental comparison, the results show that the model based on BP neural network algorithm has good accuracy and generalization ability in the evaluation of air particulate index, which makes it possible to scientifically and accurately refine the evaluation and management of urban air particulate index. The experimental results show that the air particle calibration model based on the light scattering method and improved BP neural network algorithm is practical and effective.

An Audit Risk Model Based on Improved BP Neural Network Data Mining Algorithm

Professional auditors provide audit services to businesses and they are key participants in enterprise development. Effective identification of audit risks can help auditors plan their audit work rationally and issue correct audit opinions. In the era of big data and the Internet, enterprises generate a large amount of data in their daily operations. For auditors, it is a great challenge to use data mining algorithms, machine learning, artificial intelligence, and other emerging technologies to identify high-quality audit data from the vast amount of data of audited enterprises. At the same time, some companies may falsify and modify their financial statements for their own benefit, which further increases the difficulty for auditors in conducting audits. Traditional auditing methods are costly and consuming and cannot meet standard auditing requirements. Therefore, this study applies computer data mining algorithms to construct an audit risk model that provides a reference for auditors to conduct big data analysis and mines valuable data, thereby improving the efficiency and accuracy of the audit process.

Enhancing Short-Term Prediction of BDS-3 Satellite Clock Bias Based with BSO Optimized BP Neural Network

The satellite clock bias (SCB) prediction plays an important role in high-accuracy and real-time navigation and positioning. When predicting the SCB, the performance of the BP neural network is affected by the local optimum due to inaccurate initial parameters. Therefore, we propose an improved BP neural network based on the beetle swarm optimization (BSO-BP) algorithm to improve the performance of SCB prediction in third-generation Beidou satellite navigation system (BDS-3). The proposed model takes advantage of group learning strategy to optimize the initialization parameters of the BP neural network and obtains globally optimized parameters. In order to verify the proposed BSO-BP model, 15 BDS satellites are analyzed in terms of prediction accuracy and stability of SCB. The experimental results show that when predicting 1 hour SCB based on a 12 hours SCB data, the prediction accuracy of the BSO-BP model is the best, with an average accuracy of 0.064 ns. As compared with the LP, QP, and GM models, the average prediction accuracy of the proposed BSO-BP model increases by about 72.6%, 43.4%, and 86%, respectively. As the prediction time increases, the influence of the inaccurate initial parameters on SCB prediction gradually decreases, and the prediction accuracy improves. The proposed BSO-BP model has the best accuracy and stability when predicting the 1 h SCB based on the same data. The prediction stability of the proposed BSO-BP model improves by more than 36% as compared with LP, QP, and GM models. In addition, the prediction accuracies of PHM clock and Rb-II clock improved by more than 47%, as compared with that of the Rb clock. Therefore, the overall performance of the atomic clock based on BDS-3 is better than BDS-2. The positioning accuracy of the BSO-BP model can reach the centimeter level in east, north, and up directions.

Leakage Diagnosis of Air Conditioning Water System Networks Based on an Improved BP Neural Network Algorithm

Compared with traditional pipe networks, the complexity of air conditioning water systems (ACWSs) and the alternation of cooling and heating are more likely to cause pipe network leakage. Pipe leakage failure seriously affects the reliability of the air conditioning system, and can cause energy waste or reduce human comfort. In this study, a two-stage leakage fault diagnosis (LFD) method based on an Adam optimization BP neural network algorithm, which locates leakage faults based on the change values of monitoring data from flow meters and pressure sensors in air conditioning water systems, is proposed. In the proposed LFD method, firstly, the ACWS network’s hydraulic model is built on the Dymola platform. At the same time, a cuckoo algorithm is used to identify the pipe network’s characteristics to modify the model, and the experimental results show that the relative error between the model-simulated value and the actual values is no more than 1.5%. Secondly, all possible leakage conditions in the network are simulated by the model, and the dataset is formed according to the change rate of the observed data, and is then used to train the LFD model. The proposed LFD method is verified in a practical project, where the average accuracy of the first-stage LFD model in locating the leaking pipe is 86.96%; The average R2 of the second-stage LFD model is 0.9028, and the average error between the predicted location and its exact location with the second-stage LFD model is 6.3% of the total length of the leaking pipe. The results show that the proposed method provides a feasible and convenient solution for timely and accurate detection of pipe network leakage faults in air conditioning water systems.

Risk Management of Island Petrochemical Park: Accident Early Warning Model Based on Artificial Neural Network

Island-type petrochemical parks have gradually become the ‘trend’ in establishing new parks because of the security advantages brought by their unique geographical locations. However, due to the frequent occurrence of natural disasters and difficulties in rescue in island-type parks, an early warning model is urgently needed to provide a basis for risk management. Previous research on early warning models of island-type parks seldom considered the particularity. In this study, the early warning indicator system is used as the input parameter to construct the early warning model of an island-type petrochemical park based on the back propagation (BP) neural network, and an actual island-type petrochemical park was used as a case to illustrate the model. Firstly, the safety influencing factors were screened by designing questionnaires and then an early warning indicator system was established. Secondly, particle swarm optimization (PSO) was introduced into the improved BP neural network to optimize the initial weights and thresholds of the neural network. A total of 30 groups of petrochemical park data were taken as samples—26 groups as training samples and 4 groups as test samples. Moreover, the safety status of the petrochemical park was set as the output parameter of the neural network. The comparative analysis shows that the optimized neural network is far superior to the unoptimized neural network in evaluation indicators. Finally, the Zhejiang Petrochemical Co., Ltd., park was used as a case to verify the accuracy of the proposed early warning model. Ultimately, the final output result was 0.8324, which indicates that the safety status of the case park was “safer”. The results show that the BP neural network introduced with PSO can effectively realize early warning, which is an effective model to realize the safety early warning of island-type petrochemical parks.

Fault Diagnosis of Automobile Engine Based on Improved BP Neutral Network

With the continuous enrichment of automobile functions and the complexity of automobile structure, the difficulty of automobile fault diagnosis is constantly increasing. The study of fault diagnosis methods with high real-time performance, accuracy, and predictability is of great significance to improve automobile safety performance and ensure driving safety. Since the convergence speed of the traditional BP neural network algorithm is slow and the accuracy is insufficient in the process of automobile engine fault diagnosis, this paper improves convergence speed of the algorithm by introducing the momentum term, and the weights and thresholds of the neural network are optimized by using GA selection, crossover, and genetic characteristics, to propose a genetic algorithm (GA) optimization BP neural network fault diagnosis method. The average absolute error of the traditional BP neural network algorithm is 0.5976, while the average absolute error of the improved BP neural network algorithm in this paper is 0.1027. The comparative simulation results show that the proposed improved algorithm is better than the traditional BP neural network algorithm in diagnosis precision, accuracy, and other key indicators.

Prediction and Management of the Quality of Classified Student Training Based on an Improved Neural Network

Personalized teaching and classified training promote each student to develop in the most suitable direction. The studies on classified student training (CST) mainly target the objects of CST, which refers to the classified training of underachievers and achievers, or the classified training of different groups of high-quality talents like doctoral students and master candidates. However, there are few research results on the implementation or quality forecast of CST. To fill up the gap, this paper explores the prediction and management of the CST quality based on an improved neural network. Firstly, a cognitive diagnosis model for CST was established to realize targeted group learning. Thereafter, an evaluation index system (EIS) was constructed for student learning quality. Next, a prediction model was built based on improved backpropagation neural network (BPNN), and the particle swarm optimization (PSO) was called to optimize the weights and thresholds of the neural network. The effectiveness of our model was proved through experiments. The relevant findings provide impetus to the timely update of CST.

Using Improved BPNN to Predict the Time of Social Poverty Reduction under the Background of Internet

The world has been overturned by the power of the Internet, and all walks of life are constantly changing with the trend of the times. Therefore, with the help of the effective technology and means of the Internet, people can start from a more complex perspective. Analyze the social environment and economic situation, and focus on poverty. By actively monitoring the number of poor people in society, we can grasp the actual situation of poverty. Starting from the depths of the problem, aiming at the characteristics of poor groups, this paper excavates the scale data of poor people. Finally, the improved BP neural network model is used to predict the time when the society gets rid of poverty and reduces poverty. The results show the following: (1) After P value judgment and T value test, the experiment determined 12 indicators and proportion of poverty. The most prominent indicators of contribution rate are T2, T4, and T5. The most significant changes in T test results were T8 and T12. (2) The reformed BP network has less iteration times, shorter training time, and higher prediction accuracy. The network converges 30 times, and the fitting accuracy reaches the best, and the ideal state is 0.000936578. Training the poverty alleviation data from 2016 to 2020 with the model, the final poverty alleviation work has achieved remarkable results. (3) According to the hidden layer nodes of neural network structure, the final number of hidden layer nodes is determined to be 11. (4) Comparing the absolute error and APE error of the prediction results. The predicted value is very close to the actual value, which can correctly reflect the mapping relationship of poverty reduction and the result is reliable. (5) Compare the predicted values of BPNN model before and after modification; it is found that the new BPNN model is more accurate in prediction. The predicted population is close to the actual population, and the error level curve approaches 0%. After empirical test, the improved BPNN model is accurate and effective. Through the poverty reduction prediction of the model, people can observe the actual situation more intuitively. By evaluating the effectiveness of social poverty alleviation, we can optimize the next poverty alleviation strategy and adjust the strategic policy.

Analysis of Network Public Opinion in New Media Based on BP Neural Network Algorithm

With the rapid development of Internet technology, new media is more and more favored by people and has become an important medium to control online public opinion. Social public opinion caused by new media is also more and more concerned by all walks of life. New media has the characteristics of fast information dissemination, wide dissemination range, and strong arbitrariness of news release. Positive online public voice or negative online public voice will have a very different impact on people’s lives. Some negative online public voice may even constitute a social crisis and seriously affect social public security. In order to analyze and predict the development trend of new media network public opinion, this paper presents a design of improved BP neural network model based on genetic algorithm, which is used to analyze public opinion in new media network. Experimental results show that this way has stronger processing ability and higher warning accuracy for online public opinion event index data. It can provide certain theoretical basis and data support for relevant departments to effectively prevent and manage new media network public opinion events.

A Human Resource Demand Forecasting Method Based on Improved BP Algorithm.

Human resources are the first resource for enterprise development, and a reasonable human resource structure will increase the effectiveness of an enterprise's human resource input and output. The reality is that even if an enterprise designs a human resource allocation plan in accordance with the corporate strategy, it is impossible for the enterprise to operate in full accordance with the plan during the operation process, so the human resource allocation plan only reflects the law of the enterprise's human resource needs during the enterprise development process. Giving effective guidance to the specific work of human resources is difficult. It is impossible to carry out effective human resources structure adjustment to adapt to changes in human resources demand due to changes in corporate tactics, business, scale, and other factors, especially when the current domestic human resources market has not yet fully formed. This paper examines the impact of key factors such as the company's business growth scale and production efficiency improvement on human resource needs with the goal of improving team structure, optimizing staff allocation, controlling labor costs, and improving efficiency and benefits. In this paper, we attempt to develop a human resource demand forecasting model based on business development and economic benefits and guided by intensive human resource development. We analyze and forecast the enterprise's total human resource employment, personnel structure, and quality structure using this model. In light of this, this paper employs an improved BP neural network to construct a human resource demand forecasting system, resulting in a new quantitative forecasting method for human resource demand forecasting with strong theoretical significance. Simultaneously, the human resource demand forecasting system developed can enable enterprises to carry out personnel demand forecasting from the actual situation, making forecasting more applicable, flexible, and accurate, allowing enterprises to realize their strategies through reasonable human resource planning.

Sensor Fault Diagnosis and Fault Tolerant Control of Quadrotor UAV Based on Genetic Algorithm

The quadrotor drone is small in size and light in weight, the mechanical structure is simple, and the requirements for the working environment are low. The development of a quadrotor UAV technology is also the focus of the current technical personnel. The author proposes a four-rotor UAV sensor fault diagnosis and fault-tolerant control based on genetic algorithm and introduces common sensor failures, and based on the improved BP neural network of the GA algorithm, the genetic algorithm is improved. This paper uses a classical BP algorithm, a classical GA-BP algorithm, and an improved GA-BP algorithm for training. Using a total of 150 sets of training data and training function using LevenbregMarquardt (trainlm), MeanSquaredError (performance function using mse), in the same noise background, the improved GA-BP algorithm has the highest detection rate, the classical GA-BP algorithm followed, and classical BP algorithm is the worst. Therefore, using the improved GA-BP algorithm, various errors of the sensor can be detected quickly and accurately.