Genetic BP Neural Network Research Articles

Research on Multimodal College English Teaching Model Based on Genetic Algorithm

Analyzing College English texts is essential for quantitatively evaluating their grammar, phrases, and words to enhance their use in writing, conversation, and other contexts. The precise and clear use of College English words, phrases, and sentences is essential to knowledge-based and foundational learning systems. Text data analytics run into problems with data amount, data diversity, data integration and interoperability. It is challenging to accomplish human-computer interaction in spoken College English communication and to assist students with corrections using the conventional methodology of teaching College English. Therefore, this paper proposed the Genetic Algorithm-based intelligent English course optimization system (GA-IECOS) to handle the scheduling above issue of college English classes and optimize college English teaching courses. The results demonstrate that the conventional BP neural network's local scheduling optimization issue may be resolved using the multidirectional mutation genetic BP neural network method. Subsequently, a mix of formative and summative assessments will be used to establish a couple of groups to evaluate the effectiveness using a control population and a trial group of a GA-IECOS for English language classes using a multidirectional mutation genetic algorithm and an optimization neural network. The results demonstrate that the GA-IECOS algorithm is more effective in the classroom and may greatly improve students' English performance

Research on bearing fault diagnosis based on improved genetic algorithm and BP neural network

Health monitoring and fault diagnosis of rolling bearings are crucial for the continuous and effective operation of mechanical equipment. In order to improve the accuracy of BP neural network in fault diagnosis of rolling bearings, a feature model is established from the vibration signals of rolling bearings, and an improved genetic algorithm is used to optimize the initial weights, biases, and hyperparameters of the BP neural network. This overcomes the shortcomings of BP neural network, such as being prone to local minima, slow convergence speed, and sample dependence. The improved genetic algorithm fully considers the degree of concentration and dispersion of population fitness in genetic algorithms, and adaptively adjusts the crossover and mutation probabilities of genetic algorithms in a non-linear manner. At the same time, in order to accelerate the optimization efficiency of the selection operator, the elite retention strategy is combined with the hierarchical proportional selection operation. Using the rolling bearing dataset from Case Western Reserve University in the United States as experimental data, the proposed algorithm was used for simulation and prediction. The experimental results show that compared with the other seven models, the proposed IGA-BPNN exhibit superior performance in both convergence speed and predictive performance.

Application of Genetic Algorithm-Optimized BP Neural Network in Prognosis Prediction of Hemorrhagic Stroke

Clinical intelligent diagnosis and treatment of hemorrhagic stroke, as a combination of artificial intelligence and intelligent medical treatment, is more conducive to perfectly monitoring the pathological cycle changes of patients through data flow. Based on the BP neural network model, this paper constructs a neural network model based on GABP to predict the 90-day mRS Score of patients, and successfully accurately predicts the prognosis of patients with hemorrhagic stroke. Based on the relationship between the patient's prognosis and personal history, disease history, treatment methods and imaging features, the recommendations for clinical decision-making were made. In order to test the distribution of the data, a Shapiro-Wilk distribution test model was constructed to test whether there were significant differences in the distribution of multiple populations, and finally a conclusion was reached that 90-day mRS Was not related to gender. The GABP neural network model constructed in this paper overcomes the limitation of BP neural network in complex problems, and uses genetic algorithm to optimize the weight and threshold, which improves the performance and optimization efficiency of the model. Overall, the GABP-based neural network model represents an approach that integrates neural networks and genetic algorithms and has the potential to solve complex problems. Future research and development will help to further improve the performance, interpretability and applicability of the model, thus promoting its wide application in different fields.

Using a genetic backpropagation neural network model for credit risk assessment in the micro, small and medium-sized enterprises

In China, with the "Double Carbon" goal within reach, Micro, Small and Medium-sized Enterprises (MSMEs) emerge as pivotal contributors to economic advancement. However, they are now confronted with the imperative of transitioning towards green and low-carbon practices. To facilitate the attainment of peak carbon dioxide emissions and carbon neutrality, a refined approach is imperative. This entails precise capital allocation, enhanced financial services, streamlined management, and robust risk mitigation strategies. Consequently, conducting thorough credit risk assessments for MSMEs becomes a crucial endeavor. However, obtaining substantial loans for them proves challenging due to their elusive credit ratings and potential defaults. To address this issue, this study leverages machine learning and intelligent optimization algorithms to construct a classification model for default and credit ratings of MSMEs, utilizing their daily invoice data. Specifically, twelve indicators pertaining to default and credit ratings are extracted. Subsequently, Principal Component Analysis is employed to reduce dimensionality and synthesize all pertinent information. Following this, the Genetic Algorithm-based Back Propagation Neural Network (GA-BPNN) is utilized to delineate the relationship between indicators and default, as well as credit rating, respectively. The results indicate a prediction accuracy of 0.92 for default risk and 0.86 for credit rating. This underscores the efficacy of GA-BPNN in effectively classifying the underlying default risk and credit ratings of MSMEs, offering a promising approach for decision-making.

GA-BP neural network-based nonlinear regression model for machining errors of compressor blades

Given that blade machining errors can substantially degrade aeroengine performance and reliability, there is a critical imperative to implement stringent control over blade machining errors during the manufacturing process. Nevertheless, the prevailing complexity surrounding the numerous machining error types and their obfuscated interrelationships impedes the comprehensive control of blade profile errors. Consequently, unveiling the nonlinear relationships between the distinct machining errors represents a task of paramount importance. To uncover these nonlinear relationships, this work pioneers the application of factor analysis and Genetic Algorithm-optimized BP neural networks to enable nonlinear regression for diverse blade machining errors. Specifically, this paper first constructs an orthogonal factor analysis model to reduce the dimensionality of complex, high-dimensional blade machining error data and extract underlying correlations. This analysis reveals three distinct error groups, each dominated by a latent common factor. Building on these factor analysis outputs, GA-BP neural networks are then leveraged to perform nonlinear regression for intra-group errors. Guided by the Maximal Information Coefficients (MIC) between different errors, we identify control error types within each group to serve as network inputs, with other errors designated as outputs. To prevent overfitting and accelerate convergence of the neural network, Monte Carlo method is used to augment the limited raw error dataset. The augmented data trains the GA-BP network, establishing multivariate nonlinear models with control errors as independent variables and other errors as dependent variables. The model accuracy is validated by a separate validation set. The comparisons between the predicted and true values in the validation set reveal that the relative errors are around 5 %, indicating that the models achieve an accuracy of approximately 95 %. These results demonstrate the satisfactory performance of the established models. Overall, this work elucidates previously unclear relationships between distinct blade machining errors. These new insights establish the foundation for comprehensive error control and will directly benefit blade design, manufacturing, and performance.

Lidar AOD inversion and aerosol extinction profile correction method based on GA-BP neural network

Lidar is an effective remote sensing method to obtain the vertical distribution of aerosols, and how to select the aerosol extinction-backscattering ratio (AE-BR) during the inversion process is a key step to guarantee the accuracy of the lidar inversion of aerosol optical thickness (AOD) and aerosol extinction coefficient profile (AECP). In this paper, an inversion algorithm for AOD and AECP based on a genetic BP (GA-BP) neural network is proposed. Simultaneous measurements are carried out using CE318 sun photometer and lidar, and the mapping relationship between the lidar echo signal and AOD is established based on the genetic BP (GA-BP) neural network method, which achieves the accurate inversion of AOD with an absolute error mean value of 0.0156. Based on the AOD output from the GA-BP neural network, the real-time best AE- BR to improve the inversion accuracy of AECP. Finally, practical tests show that the method achieves accurate inversion of AOD, determines the range of AE-BR from 20-50sr, realizes real-time dynamic correction of AECP, and has strong generalization ability and applicability in practical situations.

Prediction model of Wordle report based on GA-BP neural network

Natural Language Processing (NLP) plays a vital role in artificial intelligence, enabling machines to understand and generate human language. This paper proposes a novel approach to improve NLP tasks by integrating a Genetic Algorithm-based BP neural network model. The model combines the strengths of genetic algorithms and neural networks. Genetic algorithms offer effective search capabilities, while the BP neural network provides a flexible learning framework. By integrating these techniques, the proposed model aims to overcome limitations of traditional NLP models. It effectively handles the complexity of natural language data, provides efficient training by avoiding local optima, and exhibits enhanced generalization capabilities for unseen data. Extensive experiments on benchmark NLP datasets validate the effectiveness of the proposed model. Results demonstrate its superiority over state-of-the-art NLP models in terms of accuracy, efficiency, and robustness. This paper presents a novel approach to enhance NLP tasks by integrating a genetic algorithm-based BP neural network model. The model shows promising results in various NLP applications and offers advantages over traditional approaches. This research contributes to the advancement of NLP techniques, facilitating more accurate and efficient language processing systems.

The situation and transformation of sports governance in national colleges and universities under the background of double reduction

Abstract This paper aims to improve the quality of sports development by using particle swarm optimization (PSO) based long and short-term memory network (LSTM) and support vector machine (SVM) based sports performance prediction model (SPPM) to realize the shift to information technology (IT)-based sports governance in the context of the “Double Reduction”. The effects of LSTM and PSO-LSTM are evaluated by testing individual exercise physiological data like heart rate and blood oxygen values. Test the effectiveness of particle swarm optimization neural network’s sports performance prediction model with sample data and conduct comparative experiments with multiple linear regression, genetic algorithm optimization BP neural network, and firefly optimization BP neural network, respectively. Analyzing the characteristics of the extracted samples and performing a paired-sample t-test on the sample size revealed the gap between service perceptions and expectations. The analysis shows that the overall expectation mean of parents is 4.482, and the actual perceived mean is 3.966. All the indexes fail to reach the ideal level of parents, which indicates that the delayed sports service proposed by the institutional sports needs to be further adjusted.

Application of fund analysis model to identify key factors in construction projects

This study constructs a funnel analysis model by collecting relevant data to achieve fault monitoring. Back-propagation (BP) neural networks are also used to identify structural damage in construction projects, and a genetic algorithm (GA) is used to optimise BP to improve issues such as slow convergence and long time consumption. The results indicate that the difference between the third-order frequency and the first-order curvature mode is the most suitable indicator for damage warning and identification. The difference in the first-order curvature mode of adjacent measurement points of the damaged component increases with the increase in the degree of damage. Comparing the GA–BP neural network and BP neural network, the former has a smaller error in identification and better performance. The maximum and minimum relative errors of GA–BP in identifying the damage degree of the structure are 8.06 and 1.61%, respectively, meeting the accuracy requirements of the project. The identification of the key factors in construction projects based on the funnel analysis model is beneficial for identifying structural damage and ensuring the safety of engineering projects.

Prediction of Surface Residual Stresses after Laser Shock Processing on TC4 Titanium Alloy Using Different Neural Network Agent Models

Nowadays, it has become a trend to use finite element simulation instead of experimental processes, and this is widely used in the fields of structural mechanics, fluid mechanics, fracture mechanics, and so on. By replacing the experimental process with finite element simulation, we can reduce time and costs; however, when using finite element simulation, we need to define a series of settings, such as modeling, material assignment, environment settings, and many other operations. For laser shock processing intensification, the simulation experiment process is cumbersome and time-consuming. It involves performing neural network agent modeling, replacing finite element simulation with the learning and prediction capabilities of neural networks, learning by using some of the simulation results as a training sets for the neural network, and then learning by using the remaining simulation results as testing sets to test the predictive ability of the neural network agent model. TC4 titanium alloy was selected as the experimental material. Three kinds of neural network agent models, a genetic algorithm-optimized BP network, a strong classifier design based on BP_Adaboost, and an extreme learning machine, instead of finite element simulation experiments, were used to predict the residual stresses generated on the surfaces of the material under different laser shock parameters. Comparing the prediction performances of different neural network agent models, the genetic algorithm-optimized BP network shows the best prediction performance, and its prediction value matches well with the experimental value. The R2, RMSE, and MAE of the testing sets of the BP network optimized using the genetic algorithm were 0.9985, 44.4518, and 30.6285, respectively. The BP network agent model optimized using the genetic algorithm for laser shock parameters other than the 208 sets of data also had good prediction performance, and the predicted values were similar to the actual experimental results. The prediction results show that the BP network optimized using the genetic algorithm can predict the residual stresses on the surface of TC4 titanium alloy material under strengthening via laser shock processing; the genetic algorithm-optimized BP neural network agent model is more convenient and quicker compared to the finite element simulation, and the predicted value is also similar to the actual value. It can thus be used to replace finite element simulation by establishing a more convenient and quicker neural network agent model.

Prediction and optimization of tower mill grinding power consumption based on GA-BP neural network

Grinding is commonly responsible for the liberation of valuable minerals from host rocks but can entail high costs in terms of energy and medium consumption, but a tower mill is a unique power-saving grinding machine over traditional mills. In a tower mill, many operating parameters affect the grinding performance, such as the amount of slurry with a known solid concentration, screw mixer speed, medium filling rate, material-ball ratio, and medium properties. Thus, 25 groups of grinding tests were conducted to establish the relationship between the grinding power consumption and operating parameters. The prediction model was established based on the backpropagation “BP” neural network, further optimized by the genetic algorithm GA to ensure the accuracy of the model, and verified. The test results show that the relative error of the predicted and actual values of the backpropagation “BP” neural network prediction model within 3% was reduced to within 2% by conducting the generic algorithm backpropagation “GA-BP” neural network. The optimum grinding power consumption of 41.069 kWh/t was obtained at the predicted operating parameters of 66.49% grinding concentration, 301.86 r/min screw speed, 20.47% medium filling rate, 96.61% medium ratio, and 0.1394 material-ball ratio. The verifying laboratory test at the optimum conditions, produced a grinding power consumption of 41.85 kWh/t with a relative error of 1.87%, showing the feasibility of using the genetic algorithm and BP neural network to optimize the grinding power consumption of the tower mill.

Evaluation of rural ecological environment development based on PCA-GA-BP model

In the post-targeted poverty alleviation era, rural revitalization has become a common action of the whole society, strengthen the rural ecological environment governance, and the construction of beautiful countryside needs to be promoted urgently. Agricultural development, rural prosperity and farmers’ prosperity are inseparable from the support of a good ecological environment. From ecological, production, life and new energy four aspects of the rural ecological environment development evaluation index system, and then the principal component analysis screening important influence index, on the basis of the genetic algorithm and BP neural network improvement model, 31 provinces during much starker choices-and graver consequences-in rural ecological environment development, and the BP neural network and GA-BP neural network evaluation results. The results show that: (1) Generally speaking, during the 13th Five-Year Plan period, my country’s rural ecological environment development index has gradually improved, but the change range is small, the average value has risen from 0.2257 to 0.2431; The number of provinces with excellent development levels has risen from 5 to 7, and the development of rural ecological environment in Beijing, Tianjin and other provinces has risen to excellent; (2) The development of regional rural ecological environment has increased or decreased, and about three-quarters of the provinces have improved the development of rural ecological environment; (3) The development of rural ecological environment is uneven, and the difference gradually expands; (4) Compared with BP neural network, GA-BP neural network has fast convergence speed, small training, verification and overall errors, high fitting degree, and has a good evaluation effect. The research conclusions can provide a basis for the evaluation and improvement of rural ecological environment development.

Parameter identification of contact model for tank saddle ring system based on genetic algorithm and BP neural network

This paper is devoted to present an experimental identification method of tank saddle ring system parameters based on BP neural network algorithm and genetic algorithm. The contact parameters in mechanical system cannot be obtained directly through measurement, and it is inevitable that there will be errors between the parameters estimated by engineering experience and the real value. In this paper, a 6-degree-of-freedom swing experimental device of tank turret is built, the excitation source is 1Hz sine function, the contact stiffness and contact damping coefficient are identified by constructing error functions based on simulation results and data of four laser displacement sensors distributed around the saddle ring, by the combination of BP neural network and genetic algorithm. Then, the sensor data in the test results and simulation results are compared to further verify the accuracy of the turret model and parameter identification method.

Simulation of the Ozone Concentration in Three Regions of Xinjiang, China, Using a Genetic Algorithm-Optimized BP Neural Network Model

Accurate ozone concentration simulation can provide a health reference for people’s daily lives. Simulating ozone concentrations is a complex task because near-surface ozone production is determined by a combination of volatile organic compounds (VOCs) and NOx emissions, atmospheric photochemical reactions, and meteorological factors. In this study, we applied a genetic algorithm-optimized back propagation (GA-BP) neural network, multiple linear regression (MLR), BP neural network, random forest (RF) algorithm, and long short-term memory network (LSTM) to model ozone concentrations in three regions of Xinjiang, China (Urumqi, Hotan, and Dushanzi districts) for the first time by inputting wind speed, humidity, visibility, temperature, and wind direction. The results showed that the average relative errors of the model simulations in the Urumqi, Hotan, and Dushanzi districts were BP (61%, 14%, and 16%), MLR (97%, 14%, and 23%), RF (39%, 11%, and 14%), LSTM (50%, 12%, and 16%), and GA-BP (16%, 4%, and 6%) and that the significance coefficients R2 were BP (0.73, 0.65, and 0.83), MLR (0.68, 0.62, and 0.74), RF (0.85, 0.80, and 0.88), LSTM (0.78, 0.74, and 0.85), and GA-BP (0.92, 0.93, and 0.94), respectively, with the simulated values of GA-BP being the closest to the true values. The GA-BP model results showed that among the 100 samples with the same wind speed, humidity, visibility, temperature, and wind direction data, the highest simulated ozone concentrations in the Urumqi, Hotan, and Dushanzi districts were 173.5 μg/m3, 114.3 μg/m3, and 228.4 μg/m3, respectively. The results provide a theoretical basis for the effective control of regional ozone pollution in urban areas (Urumqi), dusty areas (Hotan), and industrial areas (Dushanzi) in Xinjiang.

Trajectory Optimization based on Genetic-Neural Network Algorithm

With the development of science and technology, aspherical surface has become an indispensable component in optical system. However, the aspheric surface has more complex optical properties than the spherical surface, so it restricts the rapid development of aspheric surface. In this paper, the main problem of aspherical machining is analyzed, which is“Trajectory control”, and the unique optical properties of aspherical surface are analyzed, a trajectory control algorithm based on genetic-BP neural network is proposed to solve the trajectory control in aspheric forming. The method is proved to be feasible and effective.

Retracted: Construction and Knowledge Mining of Traditional Chinese Medicine Ancient Books Bibliographic Abstracts Database Based on Genetic Algorithm and BP Neural Network

Retracted: Construction and Knowledge Mining of Traditional Chinese Medicine Ancient Books Bibliographic Abstracts Database Based on Genetic Algorithm and BP Neural Network

A Smart Knowledge Discover System for Teaching Quality Evaluation via Genetic Algorithm-Based BP Neural Network

A Smart Knowledge Discover System for Teaching Quality Evaluation via Genetic Algorithm-Based BP Neural Network

Retraction Note: Product modeling design based on genetic algorithm and BP neural network

Retraction Note: Product modeling design based on genetic algorithm and BP neural network

Health assessment method based on multi-sign information fusion of body area network

The widespread application of technologies such as the Internet of Things (IoT) and wireless sensors has promoted the development of body area networks (BAN) in the area of intelligent monitoring. However, current health assessment methods based on BAN still have problems such as a high false alarm rate and low efficiency in identifying signs and states, which not only increase the psychological burden of the ward but also bring unnecessary troubles to the medical staff. In response to this problem, this paper proposes a multi-sign parameter fusion health assessment model based on BP neural network (BPNN). Firstly, the blood pressure, heart rate, pulmonary hypertension, respiration rate, blood oxygen, and body temperature are obtained by sensors in real-time, and then these six parameters are fused by the BPNN. In addition, aiming at the problems of slow convergence speed and easy falling into a local minimum in BPNN, the structure of this model is optimized, and the influence of the number of neurons and activation function of the hidden layer on the performance of the model is explored. Results show that when the number of neurons in the hidden layer is 13 and the activation function is Logsit, the performance of the model is optimal. Among them, the recognition accuracy of the model is 95 %, and the running time is 2.798 s. Finally, comparing the recognition results of this model with support vector machines (SVM), genetic BP neural networks (GA-BPNN), and fuzzy neural networks (FNN), it is found that the accuracy of these three methods is 70 %, 70 % and 80 % respectively, which verifies the validity of the model proposed in this paper.

Research on Prediction of Housing Prices Based on GA-PSO-BP Neural Network Model: Evidence from Chongqing, China

Since 2000, the real estate industry has experienced rapid development, and at the same time, it has driven the rapid growth of housing prices, and the trend of housing prices has attracted attention. This paper integrates genetic algorithm and particle swarm algorithm to optimize BP neural network, and establishes a housing price prediction model based on mixed genetic particle swarm BP neural network. The average data of housing prices in Chongqing, China from 2000 to 2020 and several main factors affecting the trend of housing prices were selected as experimental data. Through the training and simulation prediction based on the mixed particle swarm BP neural network, the error between the predicted value and the actual value was within 0.5%, the validity and accuracy of the model are proved. At the same time, this paper predicts the average price of residential commercial housing in Chongqing in 2021, which provides a reference for the government’s macro-control and sellers to carry out residential commercial housing.