Improved BP Neural Network Model Research Articles

Improved BP Neural Network Model for Predicting Regional Agricultural Irrigation Water

Improved BP Neural Network Model for Predicting Regional Agricultural Irrigation Water

Research on Computer Aided Deformation Control Strategy for Welding of Large Long Straight Beams

<p>Long straight string beams are often used as supporting structures in large equipment. Due to the large size of string beams, welding technology is generally used to complete their production. This article first constructs the finite element model and heat source model of the chord beam based on the structure of the long straight beam, and then obtains the distribution and evolution law of the temperature field, as well as the temperature rise law of the temperature field under dynamic simulation. Based on this law data, an improved BP neural network model is used to predict the deformation of the beam. In order to verify the authenticity of the prediction results, the article finally builds a Matlab simulation experimental environment and conducts simulation experiments on the welding deformation prediction of the beam.</p> <p> </p>

Thermal deformation behavior investigation of Ti–10V–5Al-2.5fe-0.1B titanium alloy based on phenomenological constitutive models and a machine learning method

The two-phase titanium alloy Ti–10 V–5Al-2.5Fe-0.1 B was taken as the experimental material, and thermal compression experiments were carried out at a deformation temperature of 770–920 °C and a strain rate of 0.0005–0.5 s−1. An Arrhenius model, a modified Johnson-Cook model, and an improved BP neural network model based on the sparrow search algorithm (SSA-BP) model were established to predict the high temperature rheological stress of the alloy. A comparison of the prediction accuracy of the three models was made. When the partial random data in the rheological curves was used for model building and relatively independent data were used for predicting the rheological stress, the SSA-BP model had higher prediction accuracy, which exhibits the highest mean square correlation coefficient (R2) value of 0.9992 and the lowest root mean square error (RMSE) and average absolute relative error (AARE) values of 1.3031, and 2.0947 %, respectively. The ability of three models to predict the rheological stress for the new process parameters was verified. Results show that the SSA-BP model still has better prediction ability, which exhibits the highest mean square correlation coefficient (R2) value of 0.9720 and the lowest root mean square error (RMSE) and average absolute relative error (AARE) values of 5.0099, and 6.0382 %, respectively. The predicted values of SSA-BP for the rheological stress were used to construct the hot processing map. Results show that the trend of the power dissipation factor (η) value from the hot processing map predicted by SSA-BP can well agree with the microstructure evolution of the alloy.

Construction and Application of English teaching in vocational colleges Model under Output Orientation

Abstract The basic idea of teaching reform and practice is the teaching mode. This research, which is based on POA, examines how higher vocational English is taught under the direction of a new teaching model and assesses the viability and application of using POA to teach English in vocational institutions. This paper establishes an English teaching in vocational colleges model under POA, which consists of three parts: “driving,” “facilitating,” and “evaluating.”Through the improvement of the BP neural network, this research develops the English teaching in vocational colleges quality assessment model under POA, thereby verifying the efficacy of the model. The changes in students’ English achievement are explored through teaching experiments and the two-sided sigmoid curve. A value of 0.007<0.05 in the paired-sample t-test of the experimental class proves that compared with the traditional English teaching in vocational colleges model, the English teaching in vocational colleges model under POA has a more obvious promotion effect on English achievement. The improved BP neural network model produced quantitative assessments of teaching quality that are mostly in line with actual findings. The model is used to evaluate the quality of English instruction in vocational colleges under POA.

Quality Grading of River Crabs Based on Machine Vision and GA-BPNN.

The prices of different quality river crabs on the market can vary several times. Therefore, the internal quality identification and accurate sorting of crabs are particularly important for improving the economic benefits of the industry. Using existing sorting methods by labor and weight to meet the urgent needs of mechanization and intelligence in the crab breeding industry is difficult. Therefore, this paper proposes an improved BP neural network model based on a genetic algorithm, which can grade the crab quality. We comprehensively considered the four characteristics of crabs as the input variables of the model, namely gender, fatness, weight, and shell color of crabs, among which gender, fatness, and shell color were obtained by image processing technology, whereas weight is obtained using a load cell. First, mature machine vision technology is used to preprocess the images of the crab's abdomen and back, and then feature information is extracted from the images. Next, genetic and backpropagation algorithms are combined to establish a quality grading model for crab, and data training is conducted on the model to obtain the optimal threshold and weight values. Analysis of experimental results reveals that the average classification accuracy reaches 92.7%, which proves that this method can achieve efficient and accurate classification and sorting of crabs, successfully addressing market demand.

Prediction of Salt-Tolerant Earthworms’ Cultivation Conditions Based on the Robust Artificial Intelligence Model

Soil pollution is a very important field among current global ecological environmental problems. Many countries have focused their scientific research power on the process of soil remediation and biological detoxification, hoping to achieve the remediation effect of contaminated soil by means of biological free activity and survival mechanisms. These studies are meant to achieve a virtuous ecological cycle and provide a biological basis for the sustainable utilization and development of resources. The purpose of this study was: (1) to screen the best conditions for the cultivation and domestication of salt-tolerant earthworms; (2) to explore the influence (correlation) relationship between salt-tolerant earthworms’ growth variables and living environmental factors; (3) an improved BP neural network model was constructed to predict the expected values of variables such as C:N, NaHCO3:Na2CO3 and base:soil, so as to provide an initial cultivation model for earthworm-resistant cultivators. The materials used in this study are cow dung that was collected from Changchun LvYuan District PengYu farm; straw that was collected from the Key Laboratory of Comprehensive Straw Utilization and Black Land Protection; soil that was collected from ordinary soil in the experimental shed of Jilin Agricultural University. We also purchased “Daping No. 2” earthworms from Hunan Zengren Earthworm Breeding Base. In order to simulate the extreme living environment with high salinity and alkalinity, this paper prepared 0.1 mol/L and 0.15 mol/L NaHCO3 solution, 0.1 mol/L, and 0.2 mol/L Na2CO3 solutions. We mixed the above solutions according to the proportion of 0.1 mol/L NaHCO3 solution: 0.1 mol/L Na2CO3 solutions, 0.15 mol/L NaHCO3 solution: 0.1 mol/L Na2CO3 solution, 0.1 mol/L NaHCO3 solution: 0.2 mol/L Na2CO3 solutions. At the same time, we prepared the mixed environment of base material and soil (base material:soil = 1:1; base material:soil = 1:2); the base material was composed of cow dung and straw. The conclusions are as follows: (1) earthworms living under simulated conditions have stronger tolerance to the saline-alkali environment; (2) the situation of C:N = 30:1, NaHCO3:Na2CO3 = 1:1, base:soil = 1:2 is the ideal state for earthworms to survive; (3) earthworms with a high tolerance can provide more enzyme activities for the simulated environment, especially cellulase activity, urease activity, sucrase activity, and alkaline phosphatase activity; (4) compared with the ordinary practical operation, the average prediction accuracy of a three output neuron BP prediction model is 99.40% (>95%). The results of this study indicate that the BP neural training set established can be used to reduce breeding costs, and also to improve the productivity of earthworms, provide a mathematical model basis for ecological sustainable utilization and circular production between earthworms and soil, and rapidly encourage the ability of earthworms to repair contaminated soil or transform agricultural waste, providing basic data support conditions for soil ecological remediation systems and the sustainable utilization of agricultural waste.

Modeling and control of rate-dependent hysteresis characteristics of piezoelectric actuators based on analog filters

The hysteresis nonlinearity and rate dependence of piezoelectric actuators seriously affect their positioning accuracy in precise positioning. Proper compensation needs to be given to improve the positioning accuracy of the actuator. In this paper, the static hysteresis characteristics are described by improving the BP neural network model based on the adaptive particle swarm optimization algorithm, and the PI model is used as the hysteresis factor by the extended space method to realize the multi-valued mapping of hysteresis nonlinearity. And input three types of voltage signals, compare and analyze the experimental data and model prediction output, and use the maximum relative error and root mean square error as the evaluation indicators to prove the accuracy of the built model. Based on the improved BP neural network model, a second-order low-pass analog filter is introduced to construct a rate-dependent hysteresis model. Comparing and analyzing the rate-dependent hysteresis model and the static hysteresis model, the experimental results show that when the signal frequency is 10 Hz, 40 Hz, and 100 Hz, the predicted output of the rate-dependent hysteresis model is compared with that of the static hysteresis model, and the root mean square error decreases by 46.3%. %, 59.6%, and 65.1%, the model can more accurately describe the hysteresis characteristics of piezoelectric actuators. Finally, a rate-dependent hysteresis inverse model is established, and the rate-dependent hysteresis inverse model is used as a feedforward combined with a single neuron network PID control to form a composite controller, and the experimental verification is carried out. The results show that the maximum error between the composite controller and the actual expectation is only 2.15%. Therefore, the established model can accurately express the rate-dependent hysteresis characteristics of piezoelectric actuators, and the use of a composite controller can effectively reduce the accuracy error caused by the hysteresis nonlinear characteristics of piezoelectric actuators.

Early warning of enterprise financial risk based on improved BP neural network model in low-carbon economy

The concept of low-carbon economic development has led to changes in the business environment and financial environment of enterprises, leading to increased financial risks faced by enterprises. How to help enterprises better warn, prevent and control financial risks from the perspective of low-carbon economy has become a hot issue worth studying. Based on this, this paper is based on the perspective of low carbon economy, on the basis of analyzing the financing risk, investment risk, capital operation risk and growth risk faced by enterprises under the requirements of low carbon economy development. A set of financial risk management framework with clear hierarchy and strict vertical logic has been constructed. Ten financial early-warning indicators are constructed from four aspects. The risk prediction model of the indicator system is established using the research method of BPNN (Back Propagation Neural Network). The model is trained and simulated through the MATLAB neural network toolbox. After 10 indicators passed Bartlett’s correlation test, the BPNN financial early warning model was programmed using MATLAB software. The accuracy rate was 84.3%. The neural network training results show that when the layer node is 8, the best correct recognition rate can be obtained. Incorporate “low carbon” into the financial risk early warning indicator system that meets the requirements of low carbon economic development in the design of enterprise financial risk early warning indicators. This paper is expected to provide reference and reference for low-carbon economy enterprises to deal with financial risks under the new situation.

Application of improved BP neural network model in bank financial accounting

The research studies the enterprise group facing commercial banks, which are both market-oriented and socialized, and constructs an evaluation model based on the particularity of their daily financial accounting. This model can analyze the financial accounting performance in combination with the financial characteristics of commercial banks. The research will propose a BPNN model with the introduction of correlation factor analysis, select reasonable indicators according to the principle of indicator design, introduce factor analysis into the BPNN model, and optimize the model using the L2 norm. Finally, adjust the learning rate of the model training samples to dynamically change according to the increase in the number of iterations. The mean square error of the model in the training processes decreases and becomes very stable in the convergence process. The predicted value curve of the model almost coincides with the expected value curve, and the total error sum of the predicted results of the model is 0.003796, with higher accuracy and good generalization ability. The accuracy of the four models was analyzed. The accuracy of the model proposed in the study reached 95.6%, and the accuracy of the other three models were 83.1%, 86.8% and 91.2% respectively. The results show that the model has higher accuracy, reliability and effectiveness in the financial accounting of commercial banks, reduces the losses in the financial accounting of commercial banks, provides a technical basis of the monitoring for financial accounting of micro banks, and provides theoretical guidance and support for the application of BPNN model with the introduction to relevant factor analysis in practice.

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 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.

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.

Analysis and Research on the Impact of Physical Exercise on Residents' Health Based on the Improved BP Neural Network Model.

With the rapid improvement of social economy and the enhancement of people's health awareness, it is necessary to make an in-depth analysis of the rationality of physical exercise and the physical quality of residents. Hence, this study aims to explore the algorithm optimization of the improved BP model to analyze the effect of exercise intervention on improving public sports effect. K-clustering and Levenberg–Marquardt algorithm were used to construct an improved BP neural network model to determine the sample clustering center, as well as the weight and threshold of the indicators, so as to optimize the analysis algorithm of improving public sports effect. MATLAB simulation shows that under the target error conditions of 0.01, 0.005, 0.001, and 0.0001, the target error rate and iteration times of the improved BP model are better than the standard BP model, and the time consumption is shorter, which can be conducive to more accurately analyzing the changes of improving public sports effect under exercise intervention. Therefore, the improved BP model can effectively solve the problems of data clustering and result error rate adjustment in the process of improving public sports effect analysis and improve the analysis speed and accuracy.

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.

Short-term Photovoltaic Power Forecast Based on Greenhouse Energy Supply Requirements

In order to solve the problem of “disposal of light” in distributed photovoltaic power plants,this paper proposed that the energy supply of photovoltaic power plants could achieve local consumption in agricultural greenhouses, and predict the photovoltaic power generation in advance according to the load demand of greenhouses. The historical power data and meteorological data of the input variables were normalized, and the PV power prediction was performed at a time interval of 1 h for a time comparison. The prediction method based on the iterative improved BP neural network model was proposed. The prediction results showed that the prediction error was not More than 2%,could meet the energy supply stability requirements of greenhouse loads.

A Hybrid Foresting Model for the Wind Energy based on Small World Optimization Algorithm and BP Neural Network

Wind energy is a clean and pollution-free renewable energy source, but it has the characteristics of randomness and intermittentness. Therefore, it is of great economic significance and practical value to study the high-precision wind power prediction model for accurately predict the power generation of wind farms. This paper use the small world optimization algorithm (SWOA) to optimize the selection of the weights and threshold of the BP neural network, so that it has the advantages of small errors and global optimization. Then we proposes an improved BP neural network model based on the small world optimization algorithm. Afterwards, the model was applied to wind power prediction in actual wind fields. The experiment results show that SWOA can quickly and accurately find the global optimal solution of the parameters of the BP neural network model, which can further improve the SWOA-BP model to obtain better prediction performance.

Effects of cost-benefit analysis under back propagation neural network on financial benefit evaluation of investment projects.

To determine the influence of the weight of the economic effectiveness evaluation criteria of the major investments of listed enterprises, and provide new management ideas for the development of the follow-up enterprises, firstly, the financial benefit evaluation system of investment projects is analyzed and constructed, and the specific evaluation process is analyzed. Then, on this basis, the evaluation index is refined; the basic structure of BP neural network (BPNN) is introduced, and genetic algorithm is used to improve BP neural network. The cost-benefit analysis model is constructed based on the improved BPNN. The listed company A is taken as an example to analyze its development data in recent years, and then the data of 10 listed companies are taken as the research object. Matlab simulation software is used to train and verify the improved BPNN model, analyze and predict the weight value of the financial benefit index of the investment projects of these 10 companies, and then determine the index to improve the financial benefit of the investment projects. Under the analysis of the development data of listed company A in the past 10 years, it is found that the indicators of the listed company's profitability per share, debt risk operation ability, development, and growth ability in the past 10 years are in relatively stable state. The principal component analysis of its 20 secondary sub-indexes is conducted based on the four primary indicators: profitability, debt risk, operational capacity, and development and growth. A total of eight principal components including return on equity (ROE), return on assets (ROA), (total asset turnover) TATO, turnover of account receivable (AR), asset-liability ratio, interest protection multiple, income growth rate, and year-on-year rate of increase for complete assets are extracted. The average error between the final output value, the actual value, and the expected value is 0.0304 and 0.0169, respectively. The weight coefficient of the monetary benefits evaluation indicator of investment items is calculated, and the computed results show that year-on-year rate of increase for complete assets, TOTA, ROA, turnover of total capital, and ROE are important indexes in the financial benefit evaluation of investment projects. It indicates that to improve the financial benefit of investment projects of listed enterprises, it is necessary to enhance the year-over-year growth degree of total properties and ROA.

Research on Steam Cloud Explosion Overpressure Prediction Model Based on Improved BP Neural Network

In order to improve the feasibility and accuracy of steam cloud explosion overpressure prediction, a combination of factor analysis method and BP neural network method is proposed to propose an improved BP neural network prediction method. According to the original data of the main influencing factors of steam cloud explosion overpressure, the factor analysis method is used to reduce the dimensional data of the five steam cloud explosion overpressure factors, and obtain a common factor; replace the original with a common factor. As an input layer parameter of BP neural network, a steam cloud explosion overpressure prediction model is established by combining the factor analysis method with BP neural network method to predict the overpressure of steam cloud explosion. The improved BP neural network prediction method is verified by the example data. The final verification result is that the relative average error between the predicted and actual values of the 15 training samples is 2.51%, which proves that the improved BP neural network model with training has a good fitting effect. The relative errors of the five predicted samples were 0.77%, 1.34%, 2.07%, 3.96%, and 6.27%, both of which were less than 10%, which proved that the improved BP neural network prediction model has better prediction accuracy.

The usage of artificial intelligence in the commodity house price evaluation model

The current standardization and legalization of commercial housing price assessment is low. In addition, computer technology is less used in this field. Therefore, the evaluation of commercial housing prices can no longer meet the requirements of today’s economy and society. Traditional real estate assessment methods such as market comparison method, cost estimation method, income reduction method, etc., pay attention to market information, but tend to emphasize the total value of real estate in evaluation practice, and they ignore the impact of various constraints on real estate prices, so it is impossible to scientifically understand the influencing factors of real estate prices and their mechanisms of action, resulting in a lack of scientific basis for decision-making by governments, developers or property users. Therefore, this paper introduces the improved algorithm of BP neural network to establish the housing price evaluation model, which reduces the subjectivity and randomness in the evaluation process. This has certain reference for the development of China’s real estate assessment method. The evaluation results show that the real estate evaluation price of the network output is close to the actual price, with maximum error of 3.04%. This shows that the application of improved BP neural network model in real estate price evaluation is not only technically feasible, but also credible.