Optimized BP Network Research Articles

Retracted: Analysis of English Performance Rating Based on Machine Learning and Optimized BP Network Technology

Retracted: Analysis of English Performance Rating Based on Machine Learning and Optimized BP Network Technology

Calibration of Small-Grain Seed Parameters Based on a BP Neural Network: A Case Study with Red Clover Seeds

In order to enhance the accuracy of discrete element numerical simulations in the processing of small-seed particles, it is essential to calibrate the parameters of seeds within the discrete element software. This study employs a series of physical tests to obtain the physical and contact parameters of red clover seeds. A discrete element model of red clover seeds is established. Plackett–Burman Design, steepest ascent, and Central Composite Design experiments are sequentially conducted. The simulation deviation of the resting angle of red clover seeds is employed as the evaluation criterion for parameter optimization. The results indicate that the coefficients of static friction between red clover seeds, the coefficients of rolling friction between red clover seeds, and the coefficients of static friction between red clover seeds and the steel plates significantly influence the resting angle. Modeling was performed using a backpropagation neural network, a genetic algorithm–optimized BP network, particle swarm optimization, and simulated annealing. It was found that GA-BP ensured both accuracy and stability. Compared to the traditional response surface methodology, GA-BP showed better fitting performance. For the optimized red clover seed simulation, the error between the angle of repose and the physical experiment was 0.98%. This research provides new insights into the calibration of small-grain seed parameters, demonstrating the value of GA-BP for precision modeling.

Intelligent Prediction of Transformer Loss for Low Voltage Recovery in Distribution Network with Unbalanced Load

In order to solve the problem of low voltage caused by unbalanced load in the distribution network, a transformer loss intelligent prediction model under unbalanced load is proposed. Firstly, the mathematical model of a transformer with an unbalanced load is established. The zero-sequence impedance and neutral line current of the transformer are calculated by using the Chaos Game Optimization algorithm (CGO), and the correctness of the mathematical model is proved by using actual data. Then, the correlation among network input variables is eliminated by using Principal Component Analysis (PCA), so the number of network input variables is decreased. At the same time, Sparrow Search Algorithm (SSA) is used to optimize the initial weight and threshold of the BP network, and an accurate transformer loss prediction model based on the PCA-SSA-BP is established. Finally, compared with the transformer loss prediction model based on BP network, Genetic Algorithm optimized BP network (GA-BP), Particle Swarm optimized BP network (PSO-BP) and Sparrow Search Algorithm optimized BP network (SSA-BP), the transformer loss prediction model based on PCA-SSA-BP network has been proven to be accurate by using actual data and it is helpful for low-voltage recovery in the distribution network.

A Study on Prediction Skills and Reading Efficiency in College English Based on Optimized BP Networks

In this paper, an in-depth study and analysis of college English prediction skills and reading efficiency are conducted using an optimized BP network algorithm, which is designed separately, and the accuracy of scoring results is verified and the experimental results are analyzed using two datasets; finally, under the guidance of writing feedback theory and data visualization design criteria, a reasonable visualizable writing feedback is created. The learning and training of the BP neural network model revealed that there was significant information loss in the similar nearest-neighbor user dataset used for training, and there were instances where some items were rated by the target user but not by similar nearest-neighbor users. As a result, such data is useless in training, and the neural network loses a significant amount of useful information when learning. To address this issue, this paper proposes using the singular value decomposition technique for filling, which alleviates the sparsity of the filled matrix data and improves the accuracy of recommendations even more. Both scoring models constructed using 1D CNN and LSTM networks belong to the second class of models, and this type of “end-to-end” scoring model does not require feature engineering. Finally, using 650 spoken recordings and their corresponding manual scoring data, the model is trained and tested. The experimental results show that, with a smaller training dataset, the BP network model achieves a better overall scoring performance.

Intrusion Detection Method of Electric Power Information Network in Cloud Computing Environment

With the widespread application of power grid systems, the information security problems faced by power grids have become more obvious. Various internal and external intrusion attacks that occur frequently have become an important issue affecting the normal operation of power generation and operations. The purpose of this paper is to study the intrusion detection method of electric power information(PI) network in the cloud computing environment. With the help of the cloud platform’s ability to process big data, and based on the analysis of the PI network structure, a DBN optimized BP network algorithm is proposed, and the optimized BP neural network is used as a runtime classification program. Experimental results show that MR-DBN-BP has a detection rate of 96.7% for intrusion detection of PI networks, which can effectively detect intrusions and effectively protect the power dispatch system network.

Analysis of English Performance Rating Based on Machine Learning and Optimized BP Network Technology

In order to improve the intelligent effect of English performance rating, this paper analyzes the English performance rating process and builds the corresponding algorithm model based on the wisdom of teaching ideas, and it combines BP spatial network technology with spatial sampling and spatial statistical algorithms to construct the system kernel algorithm. Moreover, this paper extracts students’ English answers through text recognition and compares them with the standard library. Among them, objective questions are mainly scored directly through standard answer comparison, and subjective questions compare student scores with standard answers. In addition, this paper uses a needs analysis to construct the framework of the entire English performance rating system and evaluates the practical effect of the system constructed in the paper through experimental research. According to statistical research data, the English performance rating system in this paper is known to have certain effects.

Rapid Modeling of the Sound Speed Field in the South China Sea Based on a Comprehensive Optimal LM-BP Artificial Neural Network

Ocean sound speed is an essential foundation for marine scientific research and marine engineering applications. In this article, a model based on a comprehensive optimal back propagation artificial neural network model is developed. The Levenberg–Marquardt algorithm is used to optimize the model, and the momentum term, normalization, and early termination method were used to predict the high precision marine sound speed profile. The sound speed profile was described by five indicators: date, time, latitude, longitude, and depth. The model used data from the CTD observation dataset of scientific investigation over the South China Sea (2009–2012) (108°–120° E, 6°–8° N), which includes comprehensive scientific investigation data from four voyages. The feasibility of modeling the sound speed field in the South China Sea is investigated. The proposed model uses the momentum term, normalization, and early termination in a traditional BP artificial neural network structure and mitigates issues with overtraining and difficulty when determining the BP neural network parameters. With the LM algorithm, a fast-modeling method for the sound field effectively achieves the precision requirement for sound speed prediction. Through the prediction and verification of the data from 2009 to 2012, the newly proposed optimized BP network model is shown to dramatically reduce the training time and improve precision compared to the traditional network model. Results showed that the root mean squared error decreased from 1.7903 m/s to 0.95732 m/s, and the training time decreased from 612.43 s to 4.231 s. Finally, the sound ray tracing simulations confirm that the model meets the accuracy requirements of acoustic sounding and verify the model’s feasibility for the real-time prediction of the vertical sound speed in saltwater bodies.

Application of a Hybrid Optimized BP Network Model to Estimate Water Quality Parameters of Beihai Lake in Beijing

Nowadays, freshwater resources are facing numerous crises and pressures, resulting from both artificial and natural process, so it is crucial to predict the water quality for the department of water environment protection. This paper proposes a hybrid optimized algorithm involving a particle swarm optimization (PSO) and genetic algorithm (GA) combined BP neural network that can predict the water quality in time series and has good performance in Beihai Lake in Beijing. The data sets consist of six water quality parameters which include Hydrogen Ion Concentration (pH), Chlorophyll-a (CHLA), Hydrogenated Amine (NH4H), Dissolved Oxygen (DO), Biochemical Oxygen Demand (BOD), and electrical conductivity (EC). The performance of the model was assessed through the absolute percentage error ( A P E m a x ), the mean absolute percentage error (MAPE), the root mean square error (RMSE), and the coefficient of determination ( R 2 ). Study results show that the model based on PSO and GA to optimize the BP neural network is able to predict the water quality parameters with reasonable accuracy, suggesting that the model is a valuable tool for lake water quality estimation. The results show that the hybrid optimized BP model has a higher prediction capacity and better robustness of water quality parameters compared with the traditional BP neural network, the PSO-optimized BP neural network, and the GA-optimized BP neural network.

Prediction of Equivalent Salt Deposit Density of Insulators Using Adaptive Quantum Particle Swarm Optimization Algorithm

Background: The Equivalent Salt Deposit Density (ESDD) is the basis of determining pollution classes and mapping grid pollution areas. The influence of environmental factors on the ESDD is complex, and it is difficult to establish an accurate mathematical model to fit the nonlinear relationship between them. Methods: In order to predict effectively the ESDD, a model of Adaptive Quantum Particle Swarm Optimized BP neural network (AQPSO-BP) was proposed. In this algorithm, the encoding mechanism based on probability amplitude of quantum bits was used to expand the ergodicity of population. The position and velocity information of each particle was applied to adaptively adjust the inertia factor. At the same time, the non-linear dynamic adjustment strategy of acceleration factors and mutation operation were introduced to reduce the probabilities of trapping in the local optima solution. Results: The prediction results show that the average relative error, the mean absolute error, the mean squared error and the coefficient of determination are 0.1393%, 1.27E-04, 2.33E-06 and 0.9830, respectively; the average relative variance is 0.0171. Conclusion: Compared with the Particle Swarm Optimized BP network (PSO-BP) and Quantum Particle Swarm Optimized BP network (QPSO-BP) models, the AQPSO-BP algorithm has higher prediction accuracy and stronger generalization ability, and is suitable for evaluating the contamination level to prevent flashover on polluted insulators. Keywords: Insulator, Equivalent Salt Deposit Density (ESDD), prediction, Quantum Particle Swarm Optimization (QPSO), algorithm, adaptive, BP neural network.

Prediction Model of Silicon Content in Hot Metal Using Optimized BP Network

In the course of systematic modeling, the artificial neural networks method is studied. In allusion to the defect of grads descension of traditional back propagation network algorithms, some improving measures have been taken to determine the optimal prediction and analysis model. These measures include adaptive learning, additive momentum, reasonable selection of drive function, and using genetic algorithm to optimize the input parameters. And to learn and predict the utilization of blast furnace production data, better application result is acquired.

Bridge Health Evaluation System based on the Optimal BP Neural Network

Neural network has strong ability of pattern recognition. In consideration of the problems of the traditional pure BP neural network, such as subjecting to the randomness of initial weights, slow convergence speed, low efficiency, easy to fall into local extreme value, in this paper we proposing an optimal BP network fusing with the genetic algorithm using in bridge health assessment. The optimized BP network algorithm has a good diagnosis effect, and improves the calculation accuracy and speed of the identification of bridge structure damage.

A Method for Gas Outburst Volume Detection Based on Multi-sensor Information Fusion in the Coal Mine

The volume of the gas outburst directly affects the safety of the underground coal mining and economic benefits. Under many changing factors, the outburst volume is uncertain at the working face, thus causing the inconvenience of its detection. With the analysis of the defect of traditional BP network, this paper proposes a way of BP neural network optimization based on genetic algorithm of good global search ability. The optimized BP network has its application in integrating main information influencing the volume of gas outburst, and its volume is estimated. The result indicates that, compared with traditional BP network, the optimized BP network has faster convergence speed and higher estimation accuracy.

Network Traffic Prediction based on Particle Swarm BP Neural Network

The traditional BP neural network algorithm has some bugs such that it is easy to fall into local minimum and the slow convergence speed. Particle swarm optimization is an evolutionary computation technology based on swarm intelligence which can not guarantee global convergence. Artificial Bee Colony algorithm is a global optimum algorithm with many advantages such as simple, convenient and strong robust. In this paper, a new BP neural network based on Artificial Bee Colony algorithm and particle swarm optimization algorithm is proposed to optimize the weight and threshold value of BP neural network. After network traffic prediction experiment, we can conclude that optimized BP network traffic prediction based on PSO-ABC has high prediction accuracy and has stable prediction performance.

Genetic Optimized BP Network Method for Camera Calibration in Binocular Vision

Camera calibration is the first step of positioning using binocular vision. Owning to the approximation capability of the neural network, a complex mathematical model needed by traditional calibration methods can be avoided. However the general neural network methods have their drawbacks to reduce its accuracy. This paper presents searching algorithm for the best structure and parameters of a neural network using an improved genetic algorithm (GA). The experiments show that this method can be used to establish a mapping between 2D coordinates and 3D coordinates directly and accurately, which is better than traditional calibration and general BP network methods.

Coal Mine Gas Prediction Model Based on Ant Colony Neural Network

In order to improve the prediction accuracy and prediction speed of coal mine gas emission, ant colony algorithm combining with neural network is used for prediction models design. Choose an important factor influencing gas emission, establish of its neural network prediction model. Select the network mean square error as the objective function, through the ant colony algorithm iteration achieve optimal BP network weights, and use the optimized BP network for gas emission prediction. Simulation results show that the method has high fitting prediction accuracy.

A Study on Optimization of Automotive Suspension Base on PSO-BP Network Algorithm

A BP neural network algorithm which bases on the particle swarm optimization（PSO） is advanced in this paper, Thus formed the PSO-BP network algorithm. It makes use of PSO to reach the global optimization of BP neural network’s weight value and threshold value，and the optimized BP network is used to optimize the automotive suspension. Simulation results shows that this algorithm can improve the weak points of the BP learning algorithm such as the slow convergence rate and the poor global covergence. It can also reduce the number of training and the error obviously.

Predicting Software Quality by Optimized BP Network Based on PSO

The prediction model of software quality is the key technology in the software quality evaluation system, which can be used to evaluate software quality characteristics that users care about. Prediction models are often used to find the nonlinear relationship between metric data and quality factors. The paper predicted the relationship between metric data and quality factors with historical data by using the optimized BP network based on PSO. According to the algorithm, 28 groups of data are adopted in the experiment, and compared with the results by using BP network. Experiments show that the algorithm has a better performance than the BP network algorithm and perfectly solve the problem of slow convergence and easily getting into local minimum.

Research on System Safety Assessment Based on Optimized BP Network

The safety assessment of the production process can provide scientific basis for management decisions. The application of neural network theory in system safety assessment can overcome some traditional deficiencies of traditional safety assessment methods and arrive at accurate assessment result. The paper introduced Genetic Algorithm (GA) into the optimization of BP algorithm, which use GA to optimize the weight and threshold of BP and then utilize BP network to seek for exact solution, so that the target of global optimization and fast efficiency can be achieved. Based on this, the GA optimized neural network was applied in the system safety assessment, and the assessment model based on the algorithm was proposed. The example to verify the effectiveness of GA optimized BP algorithm was also provided.

Improved Wavelet Neural Network Based on Hybrid Genetic Algorithm Applicationin on Fault Diagnosis of Railway Rolling Bearing

The method of improved wavelet transform neural network based on hybrid GA(genetic algorithm) is presented to diagnose rolling bearings faults in this paper. Genetic Artificial Neural Networks(GA-ANN) overcomes BP neural network’s fault of slow convergence, long hours of training, and falling into the local minimum point. And First, the signal is processed through the wavelet deoising, Then, three— layer wavelet packet is adopted to decompose the denoising signal of rolling beatings, and constructs the wavelet packet energy eigenvector, then takes those wavelet packet energy eigenvectors as fault samples to train BP neural network. Genetic algorithm is used to optimize the training process of BP network. The experimental results show that the optimized BP network by genetic algorithm can diagnose bearing faults, and it is superior to the BP network without optimization, the method has fair prospects of application for the rotary machine fault diagnosis.