Improved BP Neural Network Research Articles

Detection of partial discharge patterns in hybrid high voltage power transmission lines based on parallel recognition method

Due to their specially designed structures, the partial discharge detection of hybrid high-voltage power transmission lines (HHVPTL) composed of overhead lines and power cables has made it difficult to monitor the conditions of power transmission lines. A parallel recognition method for partial discharge patterns of HHVPTLs is proposed by implementing wavelet analysis and improved backpropagation neural network (BPNN) to address the shortcomings of low efficiency, poor accuracy, and inability to parallel analysis of current partial discharge (PD) detection algorithms for HHVPTLs. Firstly, considering the non-smoothness of the partial discharge of the HHVPTLs, the wavelet packet decomposition algorithm is implemented to decompose the PD of the HHVPTL and resolve the relevant signal indicators to form the attribute vectors. Then, BPNN is implemented as a classification model. A beetle optimization (DBO) algorithm based on orthogonal contrastive learning improvement is introduced to optimize the BPNN parameters since BPNN has a slow convergence problem and fails easily into a local optimum. The proposed IDBO-BPNN is employed as the model that recognizes and analyzes the parallel partial discharge patterns of HHVPTLs. Finally, the suggested model is implemented to investigate the local discharge data of an HHVPTL in the Kaggle Featured Prediction Competition and is compared with other algorithms. The experimental results indicate that the proposed model can more accurately identify whether PDs occur in an HHVPTL and detect phases where PDs occur, with higher overall accuracy and efficiency. An excellent practical performance is achieved. The proposed model can achieve the recognition accuracy of 95.5%, which is 5.3333% higher than that of the DBO-BPNN and far more than other recognition algorithms.

An Improved Back Propagation Neural Network Based on Differential Evolution and Grey Wolf Optimizer and Its Application in the Height Prediction of Water-Conducting Fracture Zone

Given that the conventional back propagation neural network (BPNN) easily falls into the local optimal solutions, resulting in poor prediction accuracy, an improved BPNN based on the differential evolution and grey wolf optimizer (DEGWO) is proposed, the so-called DEGWO-BPNN. The prediction of the water-conducting fracture zone (WCFZ) height is significant for mine safety operations. A total of 104 sample data are trained and 25 sample data are tested to identify the optimal prediction model. Five evaluation indexes are selected to assess the prediction performance of the models quantitatively. Finally, the DEGWO-BPNN model is applied to a specific engineering case. The main conclusions are as follows: (1) Mining height, mining depth, coal seam dip, panel width, and ratio of hard rock as the main factors affecting the WCFZ height are selected. The topology structure of the model is defined as ‘5-12-1’; (2) the bias between the predicted value and the actual value of the training samples is smaller with an average error of 2.39. Test samples further validate the prediction precision through evaluation indexes. The values of MAE, RMSE, MAPE, and R2 are 2.3952, 3.4674, 5.3148%, and 0.99077, respectively. The prediction accuracy is 94.6852%; (3) ‘Mining Code’, MLR, BPNN, and GWO-BPNN models are treated as the comparison groups. The comparative analysis shows that the prediction performance of ‘Mining Code’ is the worst, while that of DEGWO-BPNN is the best, and it outperforms other algorithms and statistical approaches; (4) the prediction of WCFZ height in the 11601 panel is in line with the actual value. The prediction error of the DEGWO-BPNN model is lower than that of the comparison models. As such, the DEGWO-BPNN model can be well applied to the prediction of WCFZ height and is suitable for coal mines with different regional geological conditions. It can provide a valuable reference for mine safety operations.

The reliability analysis and experiment verification of pressure spherical model for deep sea submersible based on data BP and machine learning technology

Spherical pressure-resistant shells, as a universal structural component of deep-sea submersibles, provide a safe and normal operating environment for personnel and internal equipment. In the paper it presented and optimized the BP neural network model based on a genetic algorithm (GA) accordingly, and the method and accuracy are validated through by a beam model. Simultaneously focusing on steel spherical shells, the study proposed a dataset that captures the influence of the primary dimension of the shell (radius-to-thickness ratio, R/t) on the critical pressure response. The genetic algorithm is employed to optimize the back propagation (BP) neural network model for predicting critical pressure. The structural reliability is adopted as a design criterion to determinate and optimize the geometric parameters and critical pressure of the spherical shell structure. Finally, an ultra-high-strength steel spherical model is designed, constructed and meanwhile collapse pressure tests are accomplished to verify the accuracy of the presented improved BP neural network model based on the computational reliability method. The results reveal that the machine learning optimization design method proposed in this paper can effectively enhance the accuracy of critical pressure predictions and the precision of reliability assessments for deep-sea spherical shells.

Study on the Effect of Post-Freezing Mechanical Properties of Polypropylene Fibre Concrete Based on BAS-BPNN

An indoor accelerated freezing and thawing test of polypropylene fibre-reinforced concrete in chloride and sulphate environments was conducted using the “fast-freezing method” with the objective of investigating the damage law of the post-freezing mechanical properties of hydraulic concrete structures and studying the effects of different mixing amounts of polypropylene fibres on the mechanical properties of concrete. Furthermore, in order to reduce the cost of concrete tests and shorten the time required for conducting concrete tests, a backpropagation neural network based on a Beetle Antenna Search algorithm (BAS-BPNN) was established to simulate and predict the mechanical properties of polypropylene fibre-reinforced concrete. The accuracy of the model was verified. The results indicate that the order of improvement in the macro-physical properties of concrete due to fibre doping is as follows: PPF1.2 exhibited the greatest improvement in macro-physical properties of concrete, followed by PPF0.9, PPF1.5, PPF0.6, and PC. When the freezing and thawing medium and the number of cycles are identical, all four assessment indexes (R2, RMSE, SI, MAPE) demonstrate that the four groups of polypropylene fibre concrete exhibit superior performance to the control group of ordinary concrete. This indicates that polypropylene fibre can enhance the mechanical properties and freezing resistance of the concrete matrix, delay the process of freezing and thawing damage to the matrix, and extend the lifespan of the matrix, yet cannot prevent the ultimate failure of the matrix. The application of intelligent algorithms to optimise the parameters of an artificial neural network model can enhance its capacity to generalise and predict the mechanical properties of concrete. In terms of the coefficient of determination (R2), the Beetle Antenna Search algorithm (0.9782) outperforms the Particle Swarm Optimization (PSO; 0.9676), the Genetic Algorithm (GA; 0.9645), and the backpropagation neural network (BPNN; 0.9460). The improved backpropagation neural network based on the Beetle Antenna Search algorithm not only avoids the trap of local optimality but also improves the model accuracy while further accelerating the convergence speed. This approach can address the complexity, non-linearity, and modelling difficulties encountered during the freezing process of concrete. Moreover, it offers relatively accurate prediction outcomes at a reduced cost in comparison to traditional experimental methodologies.

Prediction of dredged soil settlement based on improved BP neural network

Based on the measured settlement data from the Reclamation Project of Eastern Hengsha Shoal, two improved BP neural networks optimized by the particle swarm optimization (PSO) algorithm and genetic algorithm (GA) respectively are employed to predict the long-term consolidation settlement of the dredged soil in the reclamation area. By comparing the prediction results of the improved neural networks with those obtained from standard BP neural network and various curve fitting methods including hyperbola method, Hoshino method, and Asaoka method, as well as field-measured data, the improved neural network methods are validated to have a better predictive performance. The error analysis results indicate that compared with the standard BP neural network and curve-fitting prediction methods, the improved BP neural networks exhibit smaller prediction errors in terms of MAE, MSE, RMSE, MAPE, etc., and have a closer match between the predicted settlement values and the actual values. The selected GA-BP and PSO-BP neural network prediction methods in this study demonstrate higher prediction accuracy. Meanwhile, GA-BP demonstrates higher prediction accuracy and generalization ability than PSO-BP, but PSO-BP shows faster convergence in terms of prediction speed. The preferred methods presented in this paper exhibit good robustness and high reliability, which can achieve accurate prediction of dredged soil settlement. Additionally, unlike the traditional curve-fitting methods, the selected methods can effectively reduce the influence of subjective factors during the settlement prediction, and provide a new approach for settlement prediction, which can be effectively utilized as a substitute for long-term settlement monitoring and give reference for the design of settlement control of foundation of further construction projects.

Measurement and modeling of moisture equilibrium and methane adsorption in shales from the southern Sichuan Basin

The adsorption characteristic of methane in water-bearing shale is essential for the accurate estimation of shale gas reserves. The sorption mechanisms of moisture and methane in shale remain poorly understood. In this work, moisture equilibrium measurements in shales were conducted to study moisture absorption characteristics. The governing factors for moisture absorption were determined, and the suitability of moisture absorption models was evaluated. An innovative water saturation apparatus and an improved methane adsorption system were built to measure methane adsorption in high water-bearing shales. The suitability of the Langmuir model in describing methane adsorption in water-bearing shale was studied. A predictive model for methane adsorption in water-bearing shale was developed based on improved BP neural network algorithm, which was further utilized to determine the primary governing factors for methane adsorption. The results revealed that the equilibrated moisture content in shale presents a three-stage and S-type ascending trend with increasing relative humidity. The methane adsorption amount shows a slide-type downward trend with the increase of water content. The Peleg model demonstrates the superior performance in describing the hygroscopic isotherms, and the Langmuir model performs poorly in characterizing methane adsorption in shales with high water contents. The controlling factors for the ultimate moisture content mainly include specific surface area, average pore diameter, carbonate and pyrite content. The methane adsorption in water-bearing shale is mainly governed by water content and specific surface area. The results of this work enrich the understanding of the adsorption mechanisms of shale gas and provide a solid basis for the assessment of shale gas reserves.

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>

Research on optimization of key thermal parameters of the secondary loop of PWR based on improved BP neural network

The simulation and optimization time of traditional secondary loop thermal parameters is relatively long, and the computational cost is generally high. To solve this problem, we construct an accurate model of the full-power steady-state operation of the secondary loop of the PWR, and use meta-learning combined with Bayesian optimization to realize the adaptive optimization of BP neural network hyperparameters. The effectiveness of the method is verified by designing comparative experiments by taking thermal efficiency as the optimization goal and high-pressure heater end difference and other parameters as the optimization variables, and using the improved BP neural network to optimize the accurate model. The results show that the improved secondary loop thermal parameter prediction and optimization framework can quickly and accurately predict the thermal efficiency and optimize the thermal parameters, and has good generalization ability, which lays a foundation for improving the thermal efficiency of nuclear power plants.

Failure prediction of mechanical system based on meta‐action

AbstractHighly reliable mechanical systems can lead to significant losses in the event of failure, and the lack of comprehensive failure data presents challenges for developing techniques such as critical part identification and failure prediction. In light of this, this paper proposes a meta‐action‐based fault prediction method that effectively addresses the issue of limited fault data. Initially, the mechanical system is decomposed utilizing the “Function‐Motion‐Action” (FMA) methodology to derive individual meta‐action units (MAUs). Subsequently, the limited sample of fault data from the mechanical system is combined with processed expert knowledge to construct the corresponding fault propagation‐directed graph. Furthermore, the key MAUs are determined by applying the Decision–Making Trial and Evaluation Laboratory (DEMATEL) method. Last, the degradation data of the key MAUs is acquired by monitoring them, and a non‐homogeneous discrete grey model (DNGM) integrated with an improved BP neural network is proposed to facilitate the fault prediction of MAUs. Using an industrial robot as a case study, the prediction results demonstrate the superiority of the method proposed in this paper over a single gray model and neural network, thereby providing a reliable prediction approach for anticipating the future trends of data‐deficient mechanical systems.

Kinematics analysis of a novel hybrid manipulator for optomechanical modules assembly

A novel hybrid manipulator is proposed in this work, in order to assemble optomechanical modules in an inertial confinement fusion facility (SG-Ⅲ). Differ from general serial and parallel manipulators, both the inverse and forward kinematics problems of hybrid manipulators are difficult to be solved. Based on an analytic method, the position and orientation mapping functions between the optomechanical module and the parallel manipulators are established, and then the method of transformation matrix is employed to obtain the inverse kinematics solution. The forward kinematics of this hybrid manipulator is highly nonlinear and coupled, so a back propagation neural network using all class one network strategy is trained to approximate the mapping relation. Aiming to improve the accuracy, the network is split into two sub-networks corresponding to the natural property of the outputs. In the end, an improved back propagation neural network is proposed to optimize the network, results illustrate that the kinematic accuracy is increased significantly.

Retracted: Supply Capability Evaluation of Intelligent Manufacturing Enterprises Based on Improved BP Neural Network

Retracted: Supply Capability Evaluation of Intelligent Manufacturing Enterprises Based on Improved BP Neural Network

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.

A train F-TR lock anti-lifting detection method based on improved BP neural network

In the railway container yard, there are few mature intelligent lifting prevention solutions available for train flatbed loading and unloading operations due to the poor detection accuracy or speed of traditional detection methods. This paper designs a train Flatbed Twist Rail (F-TR) lock anti-lifting detection method based on an improved BP neural network. The system collects weight and laser distance measurement data from the four locks of the hoist, establishes a flatbed lifting detection model based on the BP neural network, and optimizes the model's performance by incorporating a momentum factor and adaptive learning rate during weight adjustment. In practical tests, this system demonstrates a high detection rate and fast detection speed, offering intelligent safety protection for automated rail mounted gantry in the railway container yard.

A Method for Predicting Ground Pressure in Meihuajing Coal Mine Based on Improved BP Neural Network by Immune Algorithm-Particle Swarm Optimization

Based on the background of dynamic mining pressure monitoring and pressure prediction research on the No. 232205 working face of the Meihuajing coal mine, this study systematically investigates the predictive model of mining pressure manifestation on the working face of the Meihuajing coal mine by integrating methods such as engineering investigation, theoretical analysis, and mathematical modeling. A mining pressure manifestation prediction method based on IA-PSO-BP is proposed. The IA-PSO optimization algorithm is applied to optimize the hyperparameters of the BP neural network, and the working face mining pressure prediction model based on IA-PSO-BP is established. The mean absolute error (MAE), mean square error (MSE), and coefficient of determination (R2) are selected as evaluation indicators to compare the prediction performance of the BP model, PSO-BP model, and IA-PSO-BP model. The experimental results of the model show that the convergence speed of the IA-PSO-BP model is about eight times faster than that of the BP model and two times faster than that of the PSO-BP model. Compared with the BP and PSO-BP models, the IA-PSO-BP model has the smallest MAE and MSE and the largest R2 on the three different data sets of the test set, indicating significantly improved prediction accuracy. The predicted results conform to the periodic variation pattern of mining pressure data and are consistent with the actual situation in the coal mine.

An improved Back Propagation Neural Network framework and its application in the automatic calibration of Storm Water Management Model for an urban river watershed

The use of the Storm Water Management Model (SWMM) to simulate flows in urban river watersheds necessitates the proper calibration of the various parameters involved in the process. Back Propagation Neural Network (BPNN) is often used to establish relationship between two sets of multivariate variables, such as parameters and simulation results of SWMM. The aim of this study is to establish an improved BPNN to calibrate SWMM. It was found that when using gauged flow data obtained from the urban river management system as calibration data, only using BPNN was not sufficient. An improved BPNN framework was proposed with integrating Principal Component Analysis (PCA) and Genetic Algorithm (GA) process, abbreviated as PCA-GA-BPNN. It was proved to be effective for calibration. The BPNN combined with GA process made 90 % of the predicted parameters within reasonable range, which was only 8 % using BPNN alone. The PCA process reduced the training time up to 64 %. Using a hydrograph of 196 h, compared with the nondominated sorting genetic algorithm (NSGA), PCA-GA-BPNN training time can be reduced from 18,142 s down to 4.5 s. Nash efficiency coefficients (NSE) of hydrographs fitting was 0.75. Including more rainfall events data in calibration achieved better fitting than including more gauging station data.

ADAS Simulation Result Dataset Processing Based on Improved BP Neural Network

The autonomous driving simulation field lacks evaluation and forecasting systems for simulation results. The data obtained from the simulation of target algorithms and vehicle models cannot be reasonably estimated. This problem affects subsequent vehicle improvement and parameter calibration. The authors relied on the simulation results of the AEB algorithm. We selected the BP Neural Network as the basis and improved it with a genetic algorithm optimized via a roulette algorithm. The regression evaluation indicators of the prediction results show that the GA-BP neural network has better prediction accuracy and generalization ability than the original BP neural network and other optimized BP neural networks. This GA-BP neural network also fills the Gap in Evaluation and Prediction Systems.

Designing and Evaluating Learning Effectiveness of Blended Teaching and Learning of College English Based on BOPPPS Modeling

Abstract This paper applies the parallel K-means clustering algorithm of adaptive cuckoo search to mine the data related to BOPPPS blended English teaching and designs the evaluation index system of BOPPPS blended English teaching based on the teaching data. Then, it constructs the teaching effect evaluation model based on an improved BP neural network, completes the evaluation of the BOPPPS blended English teaching effect, optimizes the design of university English blended teaching based on the BOPPPS model and evaluates the learning effectiveness based on teaching experiments. The students in the experimental class were clustered into three categories before the experiment: “excellent students” (17.77%), “average students” (55.56%), and “failing students” (26.67%). 26.67%). Students in classes A and B increased by 1.22 and 0.16 points, respectively. In comparison, the progress of students in class A is more obvious than that of students in class B (P=0.026<0.05), indicating that there is a significant difference between the performance of class A and class B. The evidence suggests that BOPPPS blended English teaching can significantly enhance students’ performance, and the quality of instruction is superior.

Corporate Fraud Detection Based on Improved BP Neural Network

Corporate Fraud Detection Based on Improved BP Neural Network

Investment risk assessment based on improved BP neural network

Investment risk assessment based on improved BP neural network

Investment risk assessment based on improved BP neural network

Investment risk assessment based on improved BP neural network