Thresholds Of BP Neural Network Research Articles

Construction of Online Course Resource Library Based on MEA-BP Neural Networks

Abstract In this paper, the MEA algorithm is applied to the initial optimization of the weight threshold of BP neural network, which improves the stability of the classification of course resources through a two-stage optimization search process. Secondly, the effective recognition of knowledge point entities of teaching resources is realized by combining the LSTM-CRF entity recognition model, the BERT pre-trained language model and the text data enhancement technique based on the maximum bi-directional matching algorithm. In addition, the article proposes a candidate knowledge point entity generation method based on Levenshtein Distance and an entity characterization method based on BERT-TextCNN, which helps to alleviate the noise problem of the text data of curriculum resources and enhance the entity linking effect. The results of the study show that this newly constructed online course resource repository can significantly improve the allocation level of educational resources and reduce the variability of resource allocation among colleges and universities. The coefficient of educational resource allocation variation for SN217 in the university district was reduced from 0.764 to 0.518.In addition, the mean values of this repository in terms of learning path, perceived usefulness, course resources, and intention to use ranged from 1.45 to 1.66.The study in this paper provides valuable methods and insights for optimizing online educational resources.

Task‐oriented resource prediction and adjustment algorithm for space‐based information network

SummaryThe tasks of a space‐based information network are complex and diverse, but the resources of a space‐based environment are minimal. The existing methods are challenging to match task demand to resource supply accurately. Aiming at the problem of accurate prediction from task to resource, we propose a resource prediction adjustment strategy. First, we propose a multidimensional resource prediction algorithm based on improved particle swarm optimization and back propagation (IPSO‐BP) neural network. The improved PSO is used to optimize the weight and threshold of BP neural network to make up for the defects that BP neural network is easy to fall into local minimum and the predicted output value is not unique. Second, to meet the quality of service (QoS) of tasks, we propose a density‐based performance evaluation algorithm (DPEA) to adjust resources. This method uses the idea of local sensitive hash to select the evaluation subset for the configuration task, then dynamically selects the nearest neighbors of the configuration task, and uses the idea of weighted average to evaluate the QoS performance index of the configuration task. Simulation results show that the proposed resource prediction and adjustment strategy effectively reduces the scheduling time overhead and improves resource utilization.

Photovoltaic MPPT algorithm based on adaptive particle swarm optimization neural-fuzzy control

Since the BP neural network has poor performance and unstable learning rate in the maximum power point tracking (MPPT) algorithm of photovoltaic (PV) system, an adaptive particle swarm optimization BP neural network-fuzzy control PV MPPT algorithm (APSO-BP-FLC) is proposed in this paper. First, the inertia weight, learning factor and acceleration factor of particle swarm optimization (PSO) are self-updating, and the mutation operator is adopted to initialize the position of each particle. Second, the APSO algorithm is used to update the optimal weight threshold of BP neural network, where the input layer is irradiation and temperature, and the output layer is the maximum power point (MPP) voltage. Third, the fuzzy logical control (FLC) is employed to adjust the duty cycle of Boost converter. The inputs of FLC are voltage difference and duty ratio D(n-1) at the previous time, and the output is duty ratio D(n). Moreover, D(n-1) is optimized by |dP/dU| to improve the search range of FLC. The irradiation, temperature and MPP voltage of PV cell are adopted as the datasets for simulation in a city in Shaanxi province, China. Simulation results show that the proposed MPPT algorithm is superior to the APSO-BP, FLC and perturbation and observation (P&O) algorithm with tracking performance, steady state oscillation rate and efficiency. In addition, the efficiency of proposed MPPT algorithm is improved by 0.37%, 6.2%, and 6.8% as compared to APSO-BP, FLC and P&O algorithm.

Application of Improved Deep Belief Network Based on Intelligent Algorithm in Stock Price Prediction

In order to improve the prediction accuracy of stock price, an improved model QPSO-DBN-GABP using quantum particle swarm optimization algorithm to optimize deep belief network is proposed. In this model, the quantum particle swarm optimization algorithm is used to find the optimal combination of the number of neurons in each layer of RBM, and the genetic algorithm is used to optimize the initial weight and threshold of BP neural network, so as to obtain the optimized combination prediction model. The prediction results are compared with those of DBN, PSO-DBN, and QPSO-DBN models. Through the comparison of experimental results, it is found that compared with the above three prediction models, the prediction error index RMSE of the model is reduced by about 10.1%, 9.1%, 1.3%, and MAE is reduced by 8.1%, 5.7%, and 0.67%. The prediction accuracy of the model is improved to 96.435%.

A machine learning-based approach for product maintenance prediction with reliability information conversion

Predictive maintenance (PdM) cannot only avoid economic losses caused by improper maintenance but also maximize the operation reliability of product. It has become the core of operation management. As an important issue in PdM, the time between failures (TBF) prediction can realize early detection and maintenance of products. The reliability information is the main basis for TBF prediction. Therefore, the main purpose of this paper is to establish an intelligent TBF prediction model for complex mechanical products. The reliability information conversion method is used to solve the problems of reliability information collection difficulty, high collection cost and small data samples in the process of TBF prediction based on reliability information for complex mechanical products. The product reliability information is fully mined and enriched to obtain more reliable and accurate TBF prediction results. Firstly, the Fisher algorithm is employed to convert the reliability information to expand the sample, and the compatibility test is also discussed. Secondly, BP neural network is used to realize the final prediction of TBF, and PSO algorithm is used to optimize the initial weight and threshold of BP neural network to avoid falling into local extreme value and improve the convergence speed. Thirdly, the mean-absolute-percentage-error and the Coefficient of determination are selected to evaluate the performance of the proposed model and method. Finally, a case study of TBF prediction for a remanufactured CNC milling machine tool (XK6032-01) is studied in this paper, and the results show that the feasibility and superiority of the proposed TBF prediction method.

Research on the Multimodal Digital Teaching Quality Data Evaluation Model Based on Fuzzy BP Neural Network.

We propose in this paper a fuzzy BP neural network model and DDAE-SVR deep neural network model to analyze multimodal digital teaching, establish a multimodal digital teaching quality data evaluation model based on a fuzzy BP neural network, and optimize the initial weights and thresholds of BP neural network by using adaptive variation genetic algorithm. Since the BP neural network is highly dependent on the initial weights and points, the improved genetic algorithm is used to optimize the initial weights and thresholds of the BP neural network, reduce the time for the BP neural network to find the importance and points that satisfy the training termination conditions, and improve the prediction accuracy and convergence speed of the neural network on the teaching quality evaluation results. The entropy value method, a data-based objectivity evaluation method, is introduced as the guidance mechanism of the BP neural network. The a priori guidance sample is obtained by the entropy method. Then, the adaptive variational genetic algorithm is used to optimize the BP neural network model to learn the a priori sample knowledge and establish the evaluation model, which reduces the subjectivity of the BP neural network learning sample. To better reflect and compare the effects of the two neural network evaluation models, BP and GA-BP, the sample data were continued to be input into the original GA and BSA to obtain the evaluation results and errors; then, the evaluation results of the two evaluation models, BP and GA-BP, were compared with the evaluation results of the two algorithms, GA and BSA. It was found that the GA-BP neural network evaluation model has higher accuracy and can be used for multimodal digital teaching quality evaluation, providing a more feasible solution.

Piano Information Teaching Mode Based on Deep Learning Algorithm

In order to improve the effect of piano information teaching, a piano information teaching mode based on deep learning algorithm is proposed. The teaching objectives are divided into three levels: classroom teaching objectives, curriculum objectives, and education and training objectives. A piano information classroom integrating cloud application platform, teaching platform, resource platform, learning space, and interactive classroom is built. The previous teaching mode is optimized to build an innovative teaching mode of piano information classroom. The evaluation index system of piano informatization classroom teaching quality is constructed, and the hierarchical structure model of each evaluation index is established by using the analytic hierarchy process. The hierarchical analysis method is used to establish a hierarchical structure model of each evaluation index. The judgment matrix is determined by the nine-digit scale method. After the consistency verification of the judgment matrix, the weight of the quality evaluation of piano information classroom teaching is calculated. The new mode optimizes the weight and threshold of BP neural network in deep learning algorithm by genetic algorithm (GA). The weight of each classroom teaching quality evaluation index is input into the GA-BP neural network, and the network output result is the piano information classroom teaching quality evaluation score. The test results show that the optimal number of hidden layer nodes for the BP neural network is 7, when the GA-BP neural network iterations are 95. This method can evaluate the quality of piano information classroom teaching, with high evaluation accuracy and strong practical application.

Prediction of stock market index based on ISSA-BP neural network

Stock market index forecasting is a very tempting topic. Appropriate analysis of such a topic will provide valuable insights for investors, traders and policymakers in the appealing stock markets. In this article, an improved sparrow search algorithm is proposed to optimize the initial weight and threshold of BPNN, and is applied to the forecasting of stock market indices. The contributions of this article are mainly in the following aspects: (1) Use the sine chaos model to initialize the population; (2) In the position update formula of the discoverer, the global optimal solution of the previous generation is added, and at the same time, adaptive weights are added to coordinate the capabilities of local mining and global exploration. (3) Combining the Gaussian mutation operator and the reverse learning strategy, perturbation mutation is carried out at the optimal position to generate new solutions. (4) Use the improved sparrow algorithm (ISSA) to optimize the initial weight and threshold of BP neural network. We evaluate the performance of the proposed model on four datasets, which are SSE, SZSE, SP500 and DJI. Two types of models are used to compared with the proposed mode. One of them is the use of swarm intelligence optimization algorithms to optimize BPNN, including GA-BP, PSO-BP, ACO-BP, GWO-BP, CS-BP and SSA-BP. The others are some deep learning models. The experimental results show that the three strategies proposed in this paper greatly improve the optimization ability of the sparrow search algorithm. The ISSA-BP model achieve great success in the short-term prediction of stock prices, which can help investors predict market trends and find the right time to trade. In addition, for policymakers, the rationality of policies can be assessed through market trends, so as to better promote the development of the stock market.

Medicine Safety Assessment Method based on Dynamic Dual Optimization

<p>As people pay more and more attention to medicine safety issues, related medicine safety monitoring platforms are also rapidly popularized. However, previous work has poor accuracy and low efficiency in medicine safety assessment. In this paper, the medicine safety evaluation index system of the medicine safety monitoring platform is determined from four aspects: medicine research and development, medicine market, medicine production, and medicine uses. In order to solve the problems of the medicine safety evaluation model, such as low evaluation accuracy, slow convergence speed, and long training time, the dynamic dual optimization of PSO-BP medicine safety assessment method (OPSO-BP) is proposed. The weights and thresholds of BP neural network are optimized by the PSO algorithm to improve the quality of assessment. In addition, we optimize PSO: use the cosine function to dynamically adjust the inertia weight w and use the average optimal position of the individual in the population to replace the optimal position of the individual. It improves the problem that the evaluation model in the traditional algorithm is easy to fall into the local optimal solution due to the lack of generalization ability. In this paper, the effectiveness of OPSO-BP is verified by comparative experiments with the designed questionnaire data of medicine safety evaluation.</p> <p> </p>

Observation on Application Effect of Arterial Puncture and Catheterization under Guidance of Intelligent Medical Care Ultrasound in Clinical Anesthesia.

In clinical anesthesia and the rescue of critically ill patients, arterial puncture and catheterization are the most commonly chosen ways to establish central arterial access for patients. Invasive arterial puncture and catheterization facilitate the grasp of real-time vital sign information of patients during surgery, which strengthens patient monitoring during surgery and improves safety. However, the traditional method of arterial puncture and cannulation through palpation of the radial artery is often prone to complications related to mechanical injury, such as hemorrhage, hematoma, and accidental perforation of the artery. Studies have shown that ultrasound-guided radial artery puncture and cannulation can shorten the puncture cannulation time, reduce the incidence of complications related to puncture cannulation, and improve the success rate of puncture cannulation. In order to verify it, this paper uses the experimental group and the control group to conduct comparative experiments and uses the neural network method to evaluate the effects of the two methods. As a more mature method of artificial intelligence, BP neural network is widely used in a wide range of applications and has the characteristics of strong generalization ability and fast convergence, so we choose it as the base model. The specific work of this paper is as follows: (1) in-depth study of the relevant theory of BP neural network (BPNN), focusing on the structure of BPNN and the working principle of algorithm; the problems to be solved in the clinical anesthesia effect evaluation have laid a theoretical foundation for the establishment of an improved BPNN evaluation model in the following chapters. (2) introduce the basic principle of genetic neural network, analyze the benefits of combining genetic neural network and BPNN; introduce in detail the process of genetic algorithm to optimize the weights and thresholds of BPNN, and establish a GA-BP evaluation model. The test proves the feasibility and superiority of the model.

Prediction of Departure Flights’ Taxi-Out Time Based on Intelligent Algorithm Optimized BP

Taxi-out time is the main performance index to evaluate the operational efficiency of major airports. Scientifically and accurately predicting the taxi-out time of departure flights is very important to improve the operational efficiency and coordination decision-making ability of airport. Firstly, the quantifiable influence factors of taxi-out time and their correlation is analyzed, including the number of departure flights, the number of arrival flights, the number of flights pushed back in the same period, the taxi-out time by half-hour in average, the taxi distance, and the number of turns, etc. And then a prediction model of departure flights’ taxi-out time based on BP is constructed. Because the traditional BP neural network is sensitive to the initial weight and threshold and has poor accuracy and stability, the taxi-out time prediction model of BP neural network optimized by intelligent algorithm is proposed. Genetic algorithm (GA) and sparrow search algorithm (SSA) are used to obtain the optimal weight and threshold of BP neural network, which is verified by the actual operation data of a major airport in central and southern China for two weeks. The results show that ① the taxi-out time is strongly correlated with the airport surface traffic flow, moderately correlated with the average taxi-out time, and weakly correlated with the taxi distance and the number of turns. ② The predicted outcome of the 4-element combination prediction model which considers strong correlation and medium correlation factors is the best. After adding weak correlation factors, the prediction accuracy is reduced. ③ By obtaining the local optimal weight and threshold of neural network, intelligent optimization algorithm can effectively improve the accuracy of departure flights’ taxi-out time prediction results. ④ The prediction result of BP neural network optimized based on GA is 1.79% higher than that of MAPE before optimization, MAE is reduced by 7.4 s, and RMSE is reduced by 6.93 s. The prediction result of BP neural network optimized based on SSA is 3.05% higher than that of MAPE before optimization, MAE is reduced by 16.55 s, and RMSE is reduced by 14.31 s. Therefore, Sparrow search algorithm has better optimization effect on the model than genetic algorithm.

A Novel GA-BP Neural Network for Wireless Diagnosis of Rolling Bearing

Rolling bearings are pivotal components in industrial rotating equipment and the issue of fault will occur inevitably due to long-term abrasion. This study proposes a novel GA-BP neural network (GA-BPNN) algorithm to improve the accuracy of fault diagnosis of industrial rolling bearings. The genetic algorithm (GA) is employed to optimize the structure, initial weight and threshold of BP neural network, which can improve the ability of diagnosis and reduce the time of network training. At first, the structure of network is determined so that the optimal parameters of GA can be given, then the population of GA will be encoded. At second, the individual fitness function is calculated based on the test error norm of the BP neural network, as a criterion for distinguishing the individual from individual. The optimal weight and threshold are obtained by means of the corresponding selection, cross and variation, etc. Finally, the simulation experiment is carried out in Matlab and massive vibration experimental data of industrial rolling bearing are utilized. To verify the ability of the proposed novel GA-BPNN, compared with BP neural network algorithm (BPNN), the convergence speed and accuracy of GA-BPNN are better. The results of experiment illustrate that the optimized GA-BPNN method can identify the fault-type quicker, and has higher feasibility, which can be used to assist diagnosis of industrial bearing and improve efficiency.

Optimal BP neural network algorithm for state of charge estimation of lithium-ion battery using PSO with Levy flight

The battery state of charge (SoC) of lithium batteries for electric vehicles is highly non-linear and time-varying. The convergence speed is slow and the accuracy is low when using ordinary neural network models for SoC estimation. The particle swarm optimization (PSO) algorithm based on Levy's flight strategy (LPSO) is proposed to optimize the weights and thresholds of BP neural network, which would improve the prediction accuracy of SoC. According to the mechanism of lithium battery charging and discharging, voltage, current and temperature are selected as input vectors and SoC is selected as output vector. The comparison of the model before and after optimization is carried out by using NASA lithium battery charging and discharging data, which shows this method has better generalization ability and high prediction accuracy. It has practical application significance for SoC estimation.

An optimization technique for intrusion detection of industrial control network vulnerabilities based on BP neural network

The aim of this research is to solve the problem that the intrusion detection model of industrial control system has low detection rate and detection efficiency against various attacks, a method of optimizing BP neural network based on Adaboost algorithm is proposed. Firstly, principal component analysis (PCA) is used to preprocess the original data set to eliminate its correlation. Secondly, Adaboost algorithm is used to continuously adjust the weight of training samples, to obtain the optimal weight and threshold of BP neural network. The results show that there are 13,817 pieces of data collected in the industrial control experiment, of which 9817 pieces of data are taken as the test data set, including 9770 pieces of normal data and 47 pieces of abnormal data. In addition, as a test data set of 4000 pieces, there are 3987 pieces of normal data and 13 pieces of abnormal data. It can be seen that the average detection rate and detection speed of the algorithm of optimizing BP neural network by Adaboost algorithm proposed in this paper are better than other algorithms on each attack type. It is proved that Adaboost algorithm can effectively solve the intrusion detection problem by optimizing BP neural network.

Application of Training Load Prediction Model based on Improved BP Neural Network in Sports Training of Athletes

With the enhancement of data mining technology, competitive sports informatization has become an inevitable development trend. It has become a common phenomenon to use data mining technology to help athletes train scientifically, assist coaches in rational decision-making, and improve team competitiveness. In competitive sports, cyclists' adaptation to training has a complex relationship with their physical performance. In order to explore the correlation between data and provide better training data for athletes, this study proposes a load prediction model based on BP neural network (Back propagation, BP). Considering the local convergence and random assignment of traditional BP model, an adaptive genetic algorithm with improved selection operator is used to determine the initial weights and thresholds of BP neural network to improve the accuracy of the prediction model. The experimental results show that the improved adaptive genetic algorithm improves the overall optimization ability of the BP neural network, the improved BP neural network model has good stability in the convergence process, and the algorithm can search for better weight thresholds. Compared with the basic BP neural network prediction model, the accuracy of the optimized prediction model is increased by 11.86%, and the average error value is reduced by 26.21%, which is a guide to improve the training effect of the cycling team's competitive sports.

Security and Privacy Risk Assessment of Energy Big Data in Cloud Environment.

Considering the importance of energy in our lives and its impact on other critical infrastructures, this paper starts from the whole life cycle of big data and divides the security and privacy risk factors of energy big data into five stages: data collection, data transmission, data storage, data use, and data destruction. Integrating into the consideration of cloud environment, this paper fully analyzes the risk factors of each stage and establishes a risk assessment index system for the security and privacy of energy big data. According to the different degrees of risk impact, AHP method is used to give indexes weights, genetic algorithm is used to optimize the initial weights and thresholds of BP neural network, and then the optimized weights and thresholds are given to BP neural network, and the evaluation samples in the database are used to train it. Then, the trained model is used to evaluate a case to verify the applicability of the model.

Residual Life Prediction of Lithium Battery Based on Small Sample Data Sets

In the recycling process of lithium-ion battery, the poor external environment will lead to the degradation of battery performance, so it is necessary to predict the residual life of lithiumion battery. However, the aging cycle of lithium-ion battery is long, so it is difficult to obtain sufficient aging data in a short time. Aiming at the problem of low prediction accuracy of lithium battery residual life under the condition of small samples, a BP neural network modelling algorithm fused with expert knowledge is proposed. Firstly, indirect health factors were extracted from the aging data of lithium battery in NASA PCOE laboratory. Secondly, the initial weight and threshold of BP neural network were optimized by genetic algorithm, and the initial multiplier value of the augmented Lagrange function was set according to Spearman correlation coefficient. Finally, the expert knowledge is integrated into the training process of BP neural network through the augmented Lagrange multiplier method. Simulation results show that the proposed algorithm has higher prediction accuracy than the traditional BP neural network under the condition of small samples.

RETRACTED ARTICLE: Research on pre- and post-fund performance prediction based on improved neural network algorithm

At present, domestic and foreign scholars have systematic and perfect research on the sustainability of fund performance. However, the effect of fund forecasting still needs to be improved, and the conclusions on whether the fund performance is sustainable for different samples are quite different. This study constructs a fund prediction model based on improved neural network, uses genetic algorithm to adjust the initial weight and threshold of BP neural network, and comprehensively analyzes the parameters involved in neural network. Moreover, this study determines the optimal parameters through the training model and estimates the sustainability of the fund performance by introducing a neural network algorithm. The neural network algorithm can screen out systematic disturbances, analyze and calculate effective information, and can better screen out the effective parts of various fund performance persistence calculation methods, and does not need to set up a distribution function to take the significance test. In addition, this study selected the most appropriate network structure to take an empirical analysis.

An Early Risk Warning of Cross-Border E-Commerce Using BP Neural Network

In this paper, we propose an early warning model of credit risk for cross-border e-commerce. Our proposed model, i.e., KPCA-MPSO-BP, is constructed using kernel principal component analysis (KPCA), improved particle swarm optimization (IPSO), and BP neural network. Initially, we use KPCA to reduce the credit risk index for cross-border e-commerce. Next, the inertia weight and threshold of BP neural network are searched using MPSO. Finally, BP neural network is used for training the data of 13 different enterprises of cross-border e-commerce’s credit risk. To analyze the efficiency of our proposed approach, we use the data of five different enterprises for testing and evaluation. The experimental results show that the mean absolute error (MAE) and root mean square error (RMSE) of our model are the lowest in comparison to the existing models and have much better efficiency.

Prediction of soil moisture based on BP neural network optimized search algorithm

The accuracy of traditional soil moisture prediction method is low and the training period is long. In this paper, the BP neural network prediction model is studied, and a longicorn beetle search algorithm (BAS) optimized BP neural network prediction method is proposed. Spelman rank correlation coefficient method is used to analyze the correlation between the change of soil moisture and each variable. In this paper, evaporation, ground temperature, precipitation, air pressure, sunshine hours, air temperature and wind speed are taken as independent variables to analyze the Spelman correlation with soil moisture, and the correlation between each variable and soil moisture change is obtained. The longicorn beetle search algorithm is used to optimize the initial weight and threshold of BP neural network, and the prediction model of BAS-BP neural network is established. The soil moisture prediction model of BAS-BP is compared with different prediction models of GA-BP and BP. The results show that the average absolute error and average relative error of BAS-BP are 9.1936 and 0.1333 respectively, which is lower than that of GA-BP and BP model. The shortcomings of long training time and slow convergence speed are overcome by BAS-BP neural network, and the accuracy of prediction is improved.