GA-BP Algorithm Research Articles

Evaluation of snow-drifting influencing factors and susceptibility of transportation infrastructure lines

The construction of transportation infrastructure lines alters the natural terrain, leading to uneven redistribution of snow under the influence of snow-drifting, which can compromise road safety. With the ongoing expansion of transportation networks in cold regions, transportation engineers are increasingly focusing on snow-drifting phenomena and the associated disasters. This study, based on a GIS platform, conducts a case study of a transport infrastructure project in Xinjiang to analyze the geographical and environmental conditions along the route. Through on-site monitoring and investigation, factors related to the occurrence of snow-drifting disasters were implied. The WOE (Weight of Evidence) model was selected as the base evaluation model, and the BP-GA algorithm was applied to optimize the weights of evaluation indicators. This led to the establishment of a susceptibility evaluation index system for snow-drifting disasters, improving both the computational efficiency and the accuracy of the evaluation model. The results indicate that the evaluation accuracies of the WOE, WOE-BP, and WOE-BP-GA models were 72.32%, 75.32%, and 85.18%, respectively. The use of the GA-BP algorithm effectively captured the complex nonlinear relationships among various factors, producing evaluation results highly consistent with real-world conditions. This method may efficiently identify high-risk areas of snow-drifting along transportation infrastructure lines, providing valuable insights for disaster prevention using ArcGIS.

TQ Evaluation of Higher Vocational English Sustainable Education Based on GA Algorithm and BP Algorithm

TQ Evaluation of Higher Vocational English Sustainable Education Based on GA Algorithm and BP Algorithm

Novel method for inversion of microphysical properties of clouds using Raman lidar data

Aerosol–cloud–precipitation interactions are important in the balance of Earth’s radiation budget. To further explore the relationship between clouds and precipitation, and to improve operational weather modification, it is necessary to study the microphysical parameters of liquid water clouds. Here, an inversion method that uses a back propagation (BP) neural network based on a genetic algorithm (GA), namely a GABP, is proposed to invert cloud microphysical parameters using ground-based dual-field-of-view (FOV) Raman lidar data. To verify the feasibility of the method, long-term continuous observations were conducted in the Liupan Mountains (China). Results revealed that the proposed inversion method using the GABP is feasible for retrieving the liquid water content (LWC) and the cloud droplet effective radius after training a large number of data measured simultaneously by the Raman lidar and a microwave radiometer. When inverting LWC, the root mean square error (RMSE) of the GABP algorithm was found in the range 0–0.005, whereas the RMSE of the BP algorithm fluctuated in the range 0–0.01. It was evident that the GABP algorithm yields better inversion results and finer detail. When maintaining other variables and comparing the inversion results of signals in the inner and outer FOVs, the RMSE of the inner FOV signal was within 0.005 at near-ground heights (i.e., <2 km), whereas the outer FOV signal exceeded 0.005 at certain heights. This study developed a feasible solution for detecting characteristic cloud microphysical parameters using a Raman lidar, which could be used to study aerosol–cloud–precipitation interactions, and thereby have considerable practical importance for improving artificial rainfall operations.

Temperature prediction model for ladle furnace based on mathematical mechanisms and the GA–BP algorithm

This study addresses the prediction inaccuracy and poor adaptability of conventional temperature prediction models for ladle furnace refining. The historical production data of a steel plant were used to establish a hybrid prediction model based on mathematical mechanisms and a backpropagation neural network optimized using a genetic algorithm. The coefficient of determination ( R 2) of the hybrid model was 0.98, and the hit ratio of temperature prediction within ±5°C was 99.3%. The ladle's thermal status affected the model prediction accuracy. A steel plant with a compact production rhythm and good baking state was less affected by the ladle's thermal status. The model input variables exhibited varying degrees of influence on the end temperature of molten steel in two steel plants with the starting temperature of the molten steel entering the station having the greatest influence. For accurate temperature control and prediction in actual production, high-influence variables must be given focus to ensure their stable control and process interference must be mitigated.

Research on the Application of Project Teaching Method in the New Model of Software Engineering Course

Software engineering course is one of the core courses of computer science. The students trained in the current teaching mode can no longer meet the market demand for high-technology talents. Based on this, the research attempts to optimize the traditional software engineering teaching mode by using the project teaching method (PTM). According to the basic concept of PTM and course characteristics, the reform path of PTM in software engineering course is explored in the experiment. Then in the experiment, the indicators that affect teaching reform effect is selected and a evaluation model is built. And GA-BP algorithm is used to evaluate the effect of the evaluation model. To verify the performance of the built model and the final evaluation effect, the research results are tested from the fitness of the algorithm, error performance, prediction in the data set and other aspects. GA-BP algorithm converged when it iterated to the 18th generation, and the final fitness value was 0.61. The average error square value of GA-BP was 0.35 and the minimum error square sum of GA-BP was 0.48. Its prediction accuracy in test set and training set was 93.4% and 94.1% respectively. The maximum prediction error in the training sample was only 0.015, and the performance of the above data was better than the other three algorithms. To sum up, applying PTM to software engineering curriculum reform can achieve better teaching results.

Railway wireless train communication network scheduling based on GA-BP algorithm

Railway wireless train communication network scheduling based on GA-BP algorithm

Research on Optimization Algorithms for Educational Development of Foreign Language Teachers in Colleges and Universities under the Perspective of Digital Intelligence Empowerment

Abstract The concept of digitalization and intelligence is constantly emerging with the rapid development of information technology, and the in-depth integration of digital intelligence empowerment and teacher education development has garnered significant attention. In this paper, we first examine the stage of teacher development under digital empowerment, tracing the path of teacher education development. We then construct the digital and intelligent TPACK model, as well as the model of teacher education development ability. Subsequently, we adopt a BP neural network to evaluate teacher education development and optimize it using a genetic algorithm. After testing the model, we develop a teacher education development evaluation system based on the GA-BP algorithm, which we then apply and analyze. This paper’s optimized model reduced absolute and relative errors by 418.281 and 3.53%, respectively, compared to the traditional model, and improved the overall goodness of fit by 0.0144. Following the implementation of the system in this paper, the teacher assessment analysis revealed an average teaching ability score of 89.75 for teachers majoring in Japanese foreign language, and 90.26 for those majoring in English foreign language. Notably, the average level of English foreign language majors and their various dimension levels were both higher. Secondly, foreign language teachers with two years of training have an average teaching ability score of 91.32, which is 5.07 points higher than those with only one year of training. It proves the effectiveness of the practical application of this system, and this paper also provides suggestions for the development of teacher education by both teachers and schools, which is of practical significance for the optimization and improvement of foreign language teachers’ education in colleges and universities.

Railway wireless train communication network scheduling based on GA-BP algorithm

Railway wireless train communication network scheduling based on GA-BP algorithm

Research on evaluation system and method of MES system for intelligent manufacturing

Abstract With the development of informatization and digitalization, the transformation of intelligent manufacturing has become the focus. This paper builds an evaluation system based on the scientific evaluation method of the development level of smart manufacturing and the real demand situation. It establishes an evaluation model of intelligent manufacturing MES systems by combining the GA-BP algorithm. Then, a SeqGAN generative adversarial network is used to expand the real samples. The evaluation model was trained and validated by constructing a training model through the BP neural network, taking the sample data of evaluation indexes as the network input and the seven labels from Industry 1.0 to Industry 4.0 as the network output. The results show that the classification accuracy of the model is above 98%, and all of them have achieved good results. In the actual case evaluation, it is judged that the intelligent manufacturing MES system of Company Z is in the range of industry 3.0 to 3.5, and this result is very close to the manual evaluation of the factory field survey. The synthesis shows that the model is reasonable and effective and has important guiding significance for the evaluation of enterprise intelligent manufacturing MES systems.

Mechanical Performance Forecast for BP Neural Network Materials Optimized by Genetic Algorithm

The mechanical properties of steel materials play a crucial role in their design, selection, and application. In order to better predict the mechanical properties through chemical composition and process parameters, this paper uses genetic algorithm to optimize the BP neural network to establish a mechanical property prediction model for steel materials. The model can predict three mechanical properties, including yield strength, tensile strength, and elongation, through chemical composition and process parameters. After optimization by genetic algorithm, the problems of insufficient convergence effect, random initialization of weights and thresholds in the BP neural network model are improved, and the prediction error is significantly reduced. The experimental results show that the GA-BP algorithm model has excellent performance in predicting the mechanical properties of steel materials.

Voltage transformer metering error state prediction method based on GA-BP algorithm

Abstract The metering accuracy of the voltage transformer is related to the normal operation of the power system, and the metering results can be optimized through the prediction of the error state. In this paper, according to the generation mechanism of the measurement error of the transformer, the maximum information coefficient is used to extract the error characteristic quantity, and the measurement perturbation model is constructed by combining the ambient temperature and the secondary load factor. Due to the specificity of the ambient temperature, a BP neural network is also used to compensate for the temperature of the perturbation model, which prepares for the improved BP neural network based on a genetic algorithm to recognize the error data. Finally, the simulated operation of the three-phase voltage transformer and the measured data of the wiring substation were utilized for validation, respectively. With the help of three-phase CVT simulation, the error change of A-phase simulated CVT amplitude information at the 4001st sampling point is 0.0962%, and the error change of phase information is -4.572′.GA-BP neural network also has high sensitivity to the difficult-to-detect asymptotic error and is able to realize the error calibration of voltage transformer.

The application of fault diagnosis techniques and monitoring methods in building electrical systems – based on ELM algorithm

The reliability of modern building electrical systems are receiving increasing attention as they become more intelligent and complex. As the majority of building electrical systems use neutral point grounding, earth faults or short circuits can get worse over time and damage both the distribution system and the electrical equipment. To this end, the corresponding three phases and four categories, namely three-phase voltage, three-phase current after fault, three-phase voltage distortion rate, three-phase current distortion rate, a total of 12 dimensional fault feature vectors and 10 fault simulation types, were summarised and extracted in conjunction with the actual operating conditions of the system. Using traditional fault identification ideas and neural network algorithm as reference, a 12-dimensional fault feature vector is used as the model input to construct a building electrical fault diagnosis and detection model based on ELM algorithm. Results showed that the ELM-based model’s classification accuracy for this experimental sample was 97.56 %, its AUC was 0.92, and its RMSE was 0.3521. These figures were higher than the classification accuracy and performance of the BP algorithm and GA-BP algorithm fault diagnosis models, and they also demonstrate better robustness and generalizability. The model also has a 97.27 % correct rate in fault discrimination, while the computation time is only 0.201 s, and its fault identification and diagnosis speed is faster than other algorithmic models. At the same time, this research model has a good fault monitoring accuracy of up to 98.6 % for building electrical systems. The research can provide a more sensitive, accurate and rapid fault monitoring method for the current building electrical system. It also improves the reliability of the building electrical system in a complex environment and achieves better protection of the system. This has a certain significance for the development of the building electrical industry.

Analysis of energy consumption prediction for office buildings based on GA-BP and BP algorithm

To gain building energy consumption information during the design phase, the variance analysis to identify significant factors affecting energy consumption in China cold-region office buildings are carried out in this study. Key factors are selected, and prediction models for energy consumption in cold-region office buildings are established using BP and GA-BP algorithms. Three prediction model evaluation indexes are introduced to evaluate the prediction accuracy of the models. The results show that the maximum RMSE of the BP neural network prediction model is 0.498, and the maximum MAPE is 0.797%. Furthermore, the GA algorithm is used to optimize the BP neural network, resulting in a prediction model with a maximum RMSE of 0.359 and a maximum MAPE of 0.289%. The prediction accuracy of the GA-BP algorithm is better than that of the BP algorithm.

An Efficient and High-Precision Electromagnetic–Thermal Bidirectional Coupling Reduced-Order Solution Model for Permanent Magnet Synchronous Motors

The traditional electromagnetic–thermal bidirectional coupling model (EMTBCM) of permanent magnet synchronous motors (PMSMs) requires a long time to solve, and the temperature-induced torque change is not accounted for in the finite element (FE) numerical calculation of the EM field. This paper presents a precise and efficient EMTBC reduced-order solution model. The specific methods are as follows: First, a torque control technology based on the current injection method is proposed for determining the effect of temperature on the properties of EM materials and EM torque in an EM field, and the accuracy of the FE numerical calculation model is improved. Second, we use the improved EM field finite element numerical calculation model (FEMNCM) to analyze the correlation between the EM loss, the temperature, and the load, and we replace the FEMNCM with the EM field reduction model using the least-squares method. Then, we analyze the law of the PMSM’s internal temperature distribution. We choose the GA-BP algorithm with as few samples as possible and a high accuracy and stability to build the regression prediction model of the temperature field. We use this regression prediction model to replace the complex temperature field calculation. After analyzing the EMTBCM solution strategy, the original complex EMTBC numerical calculation model is substituted with iterations of the magnetic field reduction model and the temperature field regression prediction model. The FE numerical calculation is then used to validate the reduced-order model. The proposed model is validated through numerical simulations. The numerical results indicate that the proposed reduced-order EMTBC model in this paper is accurate and computationally efficient.

Prediction of cold start emissions for hybrid electric vehicles based on genetic algorithms and neural networks

The emission of hybrid electric vehicles deteriorates during cold start, and it is a cost-effective method to reduce pollutant emissions during cold start of hybrid electric vehicles through energy management strategies. The development of energy management strategies for hybrid electric vehicle requires an accurate cold-start emissions model. However, traditional cold-start emission models based on statistics and experience are difficult to achieve satisfactory prediction accuracy, and require a lot of experimental cost and time. The use of neural networks for cold start emission prediction of hybrid electric vehicles has been widely used because of its low cost and high prediction accuracy. In this paper, the genetic algorithm is used to optimize the weights and thresholds of the neural network, and a neural network cold-start emission prediction model for hybrid electric vehicles is built. We trained the neural network using the high temperature (40 °C), normal temperature (23 °C) and low temperature (−20 °C) cold start data of hybrid vehicles obtained in the World Light Vehicle Test Code (WLTC), studied the cold start prediction results under the different cold start temperature, studied the cold start prediction results under the different input parameters, studied the cold start prediction results of the neural network optimized by the genetic algorithm. Under different input parameters and cold start temperature, the GA-BP algorithm can accurately predict the emissions of carbon monoxide (CO), carbon dioxide (CO2), hydrocarbons (THC), nitrogen oxides (NOx) and particulate matter number (PN) under hybrid electric vehicles.

Development of Cotton Picker Fire Monitoring System Based on GA-BP Algorithm.

Due to the characteristics of the cotton picker working in the field and the physical characteristics of cotton, it is easy to burn during the operation, and it is difficult to be detected, monitored, and alarmed. In this study, a fire monitoring system of cotton pickers based on GA optimized BP neural network model was designed. By integrating the monitoring data of SHT21 temperature and humidity sensors and CO concentration monitoring sensors, the fire situation was predicted, and an industrial control host computer system was developed to monitor the CO gas concentration in real time and display it on the vehicle terminal. The BP neural network was optimized by using the GA genetic algorithm as the learning algorithm, and the data collected by the gas sensor were processed by the optimized network, which effectively improved the data accuracy of CO concentration during fires. In this system, the CO concentration in the cotton box of the cotton picker was validated, and the measured value of sensor was compared with the actual value, which verified the effectiveness of the optimized BP neural network model with GA. The experimental verification showed that the system monitoring error rate was 3.44%, the accurate early warning rate was over 96.5%, and the false alarm rate and the missed alarm rate were less than 3%. In this study, the fire of cotton pickers can be monitored in real time and an early warning can be made in time, and a new method was provided for accurate monitoring of fire in the field operation of cotton pickers.

Estimation of State of Charge and State of Health of Marine Lithium Battery based on BP Neural Network

For the sake of better ensure the safety of a marine system reliability, economy, and power, on the basis of marine lithium battery discharge data, BP neural network is able to grasp the state of charge and health in this battery module. Owing to some stochastic factors of BP neural network weights and limens, the error fluctuates greatly, so a genetic algorithm is lead into improve its results. The improved GA-BP algorithm reduces the error of SOC estimation from 4.2% to 2.3% and of SOH from 0.52% to 0.24%. It indicates that the GA-BP neural network method for forecasting SOC and SOH of the marine battery management system has minor errors and high stability, which is feasible.

Fault prediction and simulation analysis of ultra-high voltage transmission-line based on thermal energy transformation

In order to solve the traditional long-distance ultra-high voltage (UHV) transmission-line risk warning system in the design process, it neglects the classification step of early warning information, which leads to the poor effect of risk early warning, the authors propose fault prediction and simulation analysis of UHV transmission-line based on thermal energy transformation. In the hardware design part, the network video server is used to design the host control module, and the video decoding chip is used to convert the acquired analog signal into digital signal, then it is transmitted to the serial port server, and the risk warning circuit is designed based on it. In the software design, GA-BP algorithm is used to obtain the number of nodes in the middle layer, the constraint range is determined, and the warning information is classified to realize the risk warning of long-distance UHV transmission-lines. The experimental results show that the warning accuracy of the system under different conditions is high, and the highest warning effect can reach 95%. It is proved that the classification recall rate of early warning information is high, which can effectively resist interference and provide strong support for the safe operation of transmission-lines.

Optimization of Aluminum Alloy Rifled Barrel ECM Process Parameters Based on GA-BP Algorithm

Electrochemical machining process parameters will affect the surface integrity, surface roughness, service life and other properties of the workpiece. In order to realize high-efficiency and high-quality ECM of aluminum alloy rifled barrel, a method of process parameter optimization based on GA-BP neural network is proposed. The model of BP neural network is established by using MATLAB software. Machining clearance and surface roughness are objective functions. The MSE and linear regression value are analyzed. Genetic algorithm is used to optimize the connection weight and output threshold of BP neural network model. The change of fitness function and the error between predicted value and actual machining value are analyzed. The results show that the GA-BP neural network model can better predict the objective function. The optimal parameters of aluminum alloy rifled barrel ECM are: electrolyte temperature of 29.5±0.2 °C, electrolyte inlet pressure of 1.23±0.02 MPa, power supply voltage of 8.4±0.1 V, and working current of 3600±50 A.

Prediction of Real Driving Emission of Light Vehicles in China VI Based on GA-BP Algorithm

In the China VI regulations for light-duty vehicles, the (RDE) test is introduced as a supplementary test procedure. In the actual test process, the RDE results are more significantly influenced by driving behavior and vehicle type. To reduce the test cost, the NOx and PN prediction models are established based on the GA-BP method. The results showed that the coefficients of determination of the GA-BP model for NOx and PN predictions are all greater than 0.9 and are linearly highly correlated at the instantaneous emission level. At the overall emission level, the overall error of the GA-BP model is less than 7% for NOx prediction and less than 6% for PN prediction. The model has high accuracy for both instantaneous and overall emissions of light-duty vehicles. This provides practical engineering value for guiding the RDE test.