PSO-BP Neural Network Research Articles

Classification Prediction of Rockburst in Railway Tunnel Based on Hybrid PSO-BP Neural Network

Rockburst is one of the main disasters in railway tunnel construction. In order to accurately predict the rockburst intensity level of the railway tunnel, the rock stress coefficient σ θ / σ c , rock brittleness coefficient σ c / σ t , and elastic energy index W et are used as evaluation indexes of rockburst intensity, and a BP neural network rockburst prediction model based on hybrid particle swarm optimization algorithm is proposed. First, 90 groups of existing rockburst examples are selected as the basic data of the mode based on the research results at home and abroad. Then, the BP neural network is improved by using particle swarm optimization (PSO) combined with the simulated annealing algorithm. The results are obtained from the training data. Based on hybrid PSO-BP neural network, the prediction model of rockburst intensity is obtained. Finally, the model is applied to the actual railway tunnel project to verify. The results show that the model takes into account individual optimization and global optimization and can correctly and effectively predict the rockburst grade of the railway tunnel, which provides a new method for rockburst prediction of the railway tunnel.

Parameter Optimization and Fragmentation Prediction of Fan-Shaped Deep Hole Blasting in Sanxin Gold and Copper Mine

For San-Xin gold and copper mine, deep blasting large block rate is high resulting in difficulty in transporting the ore out; secondary blasting not only increases blasting costs but is more likely to cause the top and bottom plate of the underground to become loose causing safety hazards. Based on the research background of Sanxin gold and copper mine, deep hole blasting parameters were determined by single-hole, variable-hole pitch, and oblique hole blasting tests, further using the inversion method to determine the optimal deep hole blasting parameters. Meanwhile, the PSO-BP neural network method was used to predict the block rate in deep hole blasting. The results of the study showed that the optimal minimum resistance line was 1.24–1.44 m, which was lower than 1.6–1.8 m in the original blasting design, which was one of the reasons for the higher blasting block rate. In addition, the PSO-BP deep hole blasting fragmentation prediction model predicts the block rate of the optimized blasting parameters and predicted a block rate of 6.83% after the optimization of hole network parameters. Its prediction accuracy is high, and the blasting parameter optimization can effectively reduce the block rate. It can reasonably reduce the rate of large pieces produced by blasting, improve blasting efficiency, and save blasting costs for enterprises. The result has wide applicability and can provide solutions for underground mines that also have problems with blasting large block rate.

Evaluation of the Emotion Model in Electronic Music Based on PSO-BP.

Electronic music can help people alleviate the pressure in life and work. It is a way to express people's emotional needs. With the increase of the types and quantity of electronic music, the traditional electronic music classification and emotional analysis cannot meet people's more and more detailed emotional needs. Therefore, this study proposes the emotion analysis of electronic music based on the PSO-BP neural network and data analysis, optimizes the BP neural network through the PSO algorithm, and extracts and analyzes the emotional characteristics of electronic music combined with data analysis. The experimental results show that compared with BP neural network, PSO-BP neural network has a faster convergence speed and better optimal individual fitness value and can provide more stable operating conditions for later training and testing. The electronic music emotion analysis model based on PSO-BP neural network can reduce the error rate of electronic music lyrics text emotion classification and identify and analyze electronic music emotion with high accuracy, which is closer to the actual results and meets the expected requirements.

Performance Evaluation of Knowledge Sharing in an Industry-University-Research Alliance Based on PSO-BPNN.

Knowledge sharing performance is very important to evaluate the interests of industry university research alliance. Firstly, this paper puts forward the index system of knowledge sharing performance evaluation of industry university research alliance. A BP neural network (BPNN) and a PSO-improved BP neural network (PSO-BPNN) are used to establish the evaluation model, and the accuracy of the model is evaluated. The prediction accuracy is evaluated by comparing the predicted value with the target value. Through comparative analysis, it is found that the performance evaluation model based on PSO-BP neural network has high accuracy and applicability, and is an effective method of alliance knowledge sharing performance evaluation.

CAD-Assisted 3D Assembly of Automobile Electrical Switch considering PSO-BP Neural Network Algorithm

The installation of automotive electrical switches is a complex three-dimensional space assembly project which has high requirements for installation accuracy. In order to improve the installation effect of automotive electrical switches, this paper applies the PSO-BP neural network algorithm to automotive electrical switches and integrates PSO and ELM algorithms. The training speed of the ELM model is fast, the model generalizes the data well, and the noise data have little effect on the model. Moreover, this article combines simulation research to evaluate the effect of this algorithm. After confirming the performance of the effect, this paper uses a case study to study the effect of the application of the PSO-BP neural network algorithm to the automotive electrical switch. The research results show that the CAD-assisted 3D assembly system of automobile electrical switch considering PSO-BP neural network algorithm has a good effect.

Reliability and fatigue life analysis of key parts of shearer

The poor reliability of shearer restricts the development and production efficiency of thin coal seam. The dynamic characteristics of the key parts have a great influence on the reliability and fatigue life of the thin seam shearer. In order to achieve high-yield and high-efficiency production of thin coal seams, the instantaneous load on the spiral drum was calculated based on the mechanical model of the spiral drum during the cutting process and the coal sample properties. According to the structure of the shearer and the material of key parts, a rigid-flexible coupling model of the shearer was established, and the stress information of the rocker arm shell, planetary carrier and planetary shaft was obtained by dynamic simulations. The membership function between material stress and structural reliability was constructed by Gaussian membership function, the main factors affecting the reliability of shearer were analyzed by orthogonal simulation test and PSO-BP neural network. The fatigue life of rocker arm shell, planetary carrier and planetary shaft were predicted by counting the rain flow of the stress information. Measures to improve the reliability and fatigue life of the shearer were proposed based on the analysis results, and effectiveness of the method proposed in this paper was verified by the practical application.

Prediction of the Yield Performance and Failure Mode of RC Columns under Cyclic-Load by PSO-BP Neural Network

The yield performances and failure mode of reinforced concrete (RC) columns, which are critical structural performances to the design and research of engineering structures, have a significant impact on the dynamic response, the performance level, and the design of seismic ductility. The traditional empirical theoretical method used to predict the yield performances and failure mode leads to large dispersions in most cases. To better estimate the yield performances and failure mode of RC columns, this paper developed a novel neural network method. Empirical theoretical models are used to determine the input parameters of the neural network by analyzing the factors that affect the yield performance and failure mode of RC columns, and the rationality of these parameters is verified by sensitivity analysis. The back-propagation (BP) neural network method was adopted. The influence of the number of hidden neurons was studied to improve the model accuracy. Comparative analysis revealed that the prediction results of the neural network are in good agreement with the experimental results and are more accurate than other traditional empirical theoretical models. The initial parameters were optimized using particle swarm optimization (PSO), which has been proven to be superior to the genetic algorithm (GA) and sparrow search algorithm (SSA) optimization methods in terms of effectiveness and computation time. The high generalization ability of the prediction model was calibrated using the test and validation sets and another eight additional sets of experimental data. The proposed method provides a new way to predict the structural performance under seismic actions when experimental data are insufficient.

Forecast of Large Earthquake Emergency Supplies Demand Based on PSO-BP Neural Network

Forecast of Large Earthquake Emergency Supplies Demand Based on PSO-BP Neural Network

Pressure Prediction Model in Transition Ladle based on Adaptive PSO-BP Neural Network

Generally, the pressure in the transition ladle during the manufacturing of amorphous alloys could be influenced by numerous factors. Given these issues, BP neural network is employed in this paper to ensure the prediction of the pressure in the transition ladle in the production of amorphous alloys, and adaptive PSO is adopted to adjust the weights and thresholds. The particle swarm is adaptively divided by the median value, the best location is searched locally, and the flight direction and speed of each particle are adjusted to obtain good results. Finally, emulations are performed using the amorphous alloy production site data of a steel plant. The results demonstrate that the pressure prediction model has higher prediction precision in the transition ladle established by the adaptive PSO-BP neural network. This can lay a foundation for the latter pressure control settings in the transition ladle.

Short-Term Power Generation Forecasting of a Photovoltaic Plant Based on PSO-BP and GA-BP Neural Networks

With the improvement in the integration of solar power generation, photovoltaic (PV) power forecasting plays a significant role in ensuring the operation security and stability of power grids. At present, the widely used backpropagation (BP) and improved BP neural network algorithm in short-term output prediction of PV power stations own the drawbacks of neglection of meteorological factors and weather conditions in inputs. Meanwhile, the existing traditional BP prediction model lacks a variety of numerical optimization algorithms, such that the prediction error is large. Therefore, based on the PV power plant in Lijiang, considering the related factors that influence PV output such as solar irradiance, environmental temperature, atmospheric pressure, wind velocity, wind direction, and historical generation data of the PV power station, three neural network algorithms (i.e., BP, GA-BP, and PSO-BP) are utilized respectively in this work to construct a short-term forecasting model of PV output. Simulation results show that GA-BP and PSO-BP network forecasting models both obtain high prediction accuracy, which indicates GA and PSO methods can effectively reduce the prediction errors in contrast to the original BP model. In particular, PSO owns better applicability than GA, which can further reduce the errors of the PV power prediction model.

An intelligent measurement method of the resonant frequency of ultrasonic scalpel transducers based on PSO-BP neural network

In the laboratory, using traditional methods to measure the resonant frequency of transducers is inefficient and costly. For this, in this study, the feature variables determining the resonant frequency of transducer was firstly analyzed with the equivalent circuit method. Based on the input–output mapping function of back propagation (BP) neural network, the nonlinear relationship model between feature variables and the resonant frequency was established. Then, particle swarm optimization (PSO) was used to optimize the initial weight and threshold of BP neural network to obtain the best parameter combination, thereby improving the output accuracy of BP neural network. The experimental data showed that it is effective to measure the resonant frequency of the transducer by PSO-BP neural network, and the measurement error was 1 Hz. Compared to traditional measurement method, the intelligent method has the advantages of low cost, high efficiency, fast response speed, and strong robustness.

Evaluation of urban green space landscape planning scheme based on PSO-BP neural network model

The evaluation simulation of urban green space landscape planning scheme based on PSO-BP neural network model is carried out in this paper. PSO-BP neural network can combine the principle of landscape ecology, integrate more evaluation indicators of ecology and urban development into the urban green space landscape planning scheme, and simply understand and predict human behavior, so as to make a more comprehensive evaluation and prediction of the urban green space landscape planning scheme. It not only has superior memory storage and learning ability, but also can simply understand and predict human behavior, so that more influencing factors that cannot be added in the past can be considered in the scheme evaluation and analysis, and the evaluation of urban green space landscape planning scheme is more comprehensive, scientific and reasonable. Experiments show that PSO-BP neural network has smaller error and better generalization ability than BP neural network. PSO-BP neural network rating model can analyze its more reasonable proportion according to the relationship between different types of green space and indicators, and give corresponding adjustment suggestions, which has guiding significance for the modification and adjustment of urban green space landscape planning scheme.

A Study on Carbon Emission Forecasting in China Based on PSO-BP Neural Network

To address the problem that the traditional BP(Back-Propagation) neural network is prone to fall into the local optimal situation for carbon emission prediction, and thus the prediction results have large errors, this paper proposes to improve the BP neural network by using particle swarm optimization algorithm, and conducts simulation experiments with national carbon emission data. The results show that the model has a 12.1% reduction in error compared with the traditional BP neural network model, and has a better prediction effect on carbon emissions.

Construction of College English Teaching Environment Assessment Model Based on BP Neural Network and Multiple Intelligence Theory.

College English has almost always been a required course, and a college student's level of English proficiency is one of the factors used to assess their learning capacity. The quality of students' English learning is largely influenced by the level of English instruction provided in colleges. However, there are still a lot of issues with college English instruction today, the most glaring of which is that English instruction is being overly simplified, and that the methods, modes, and purposes of instruction are also very narrow. Due to this, it is challenging for most colleges and universities' English teaching levels to satisfy the requirements of high-level education. The PSO-BP neural network model, which optimizes the BP neural network (BPNN), is used in this study to build a high-precision and diversified English teaching evaluation model in order to address the aforementioned issues. According to the experimental findings, the PSO-BPNN algorithm has a relative error of just 0.29 percent and an average accuracy rate of 97.02 percent. Overall performance is superior to that of the conventional BPNN algorithm, and it is the most adaptable in terms of creating various evaluation modes.

Improved Particle Swarm Optimization-Based BP Neural Networks for Aero-Optical Imaging Deviation Prediction

In this article, an improved particle swarm optimization (IPSO) algorithm based on similarity and random mutation is raised. The diversity of particles in the population is decided by the size of the aggregation. When the aggregation degree of particles in the population surpass a certain threshold, the concept of similarity is used to measure the similarity between particles and global extremum, and the particles with higher similarity are discretized by mutation strategy. By increasing the particle swarm’s diversity, the population’s local and global search ability tend to balance. The weight and threshold of the back propagation (BP) neural networks are optimized by the IPSO algorithm. Then, the model of the improved particle swarm optimization back propagation neural network (IPSO-BP) is applied to the aero-optical imaging deviation prediction. The results show that the prediction accuracy of the IPSO-BP model is superior to the PSO-BP model, the extreme learning machine (ELM) model, and the least square support vector machine (LSSVM) model, and its convergence speed is faster than that of the PSO-BP neural network model. Finally, the application of deep learning in aero-optical imaging deviation prediction is analyzed compared with the IPSO-BP neural network model.

Initial Results of Modeling and Improvement of BDS-2/GPS Broadcast Ephemeris Satellite Orbit Based on BP and PSO-BP Neural Networks

With the rapid development and gradual perfection of GNSS in recent years, improving the real-time service performance of GNSS has become a research hotspot. In GNSS single-point positioning, broadcast ephemeris is used to provide a space–time reference. However, the orbit parameters of broadcast ephemeris have meter-level errors, and no mathematical model can simulate the variation of this, which restricts the real-time positioning accuracy of GNSS. Based on this research background, this paper uses a BP (Back Propagation) neural network and a PSO (Particle Swarm Optimization)–BP neural network to model the variation in the orbit error of GPS and BDS broadcast ephemeris to improve the accuracy of broadcast ephemeris. The experimental results showed that the two neural network models in GPS can model the broadcast ephemeris orbit errors, and the results of the two models were roughly the same. The one-day and three-day improvement rates of RMS(3D) were 30–50%, but the PSO–BP neural network model was better able to model the trend of errors and effectively improve the broadcast ephemeris orbit accuracy. In BDS, both of the neural network models were able to model the broadcast ephemeris orbit errors; however, the PSO–BP neural network model results were better than those of the BP neural network. In the GEO satellite outcome of the PSO–BP neural network, the STD and RMS of the orbit error in three directions were reduced by 20–70%, with a 20–30% improvement over the BP neural network results. The IGSO satellite results showed that the PSO–BP neural network model output accuracy of the along- and radial-track directions experienced a 70–80% improvement in one and three days. The one- and three-day RMS(3D) of the MEO satellites showed that the PSO–BP neural network has a greater ability to resist gross errors than that of the BP neural network for modeling the changing trend of the broadcast ephemeris orbit errors. These results demonstrate that using neural networks to model the orbit error of broadcast ephemeris is of great significance to improving the orbit accuracy of broadcast ephemeris.

Woodworking Tool Wear Condition Monitoring during Milling Based on Power Signals and a Particle Swarm Optimization-Back Propagation Neural Network

In the intelligent manufacturing of furniture, the power signal has the characteristics of low cost and high accuracy and is often used as a tool wear condition monitoring signal. However, the power signal is not very sensitive to tool wear conditions. The present work addresses this issue by proposing a novel woodworking tool wear condition monitoring method that employs a limiting arithmetic average filtering method and particle swarm optimization (PSO)-back propagation (BP) neural network algorithm. The limiting arithmetic average filtering method was used to process the power signal and extracted the features of the woodworking tool wear conditions. The spindle speed, depths of milling, features and tool wear conditions were used as sample vectors. The PSO-BP neural network algorithm was used to establish the monitoring model of the woodworking tool wear condition. Experiments show that the proposed limiting arithmetic average filtering method and PSO-BP neural network algorithm can accurately monitor the woodworking tool wear conditions under different milling parameters.

Optimization of injection molding of display panel based on PSO-BP neural network

aiming at the practical production problem of large thin-wall plastic parts with large warping deformation and shrinkage during injection molding, the injection process parameters were optimized by CAE technology and neural network prediction method, to obtain high quality plastic finished products. Particle swarm optimization (PSO) algorithm was used to improve the BP neural network, and based on the neural network, the prediction model between injection process parameters and warpage deformation, volume shrinkage was constructed. The min-variables of the two parameters are accurately predicted by the model, and the best injection molding parameters are obtained. Through the verification of the mold test, the molding quality of the plastic parts is improved, the production cycle of the mold is shortened, and the economic benefit of the mold production is improved.

Research on compression deformation behavior of aging AA6082 aluminum alloy based on strain compensation constitutive equation and PSO-BP network model

It is important to model the flow behavior of an aged aluminum alloy AA6082 as an extrusion material before design and optimize of the forming process. In this study, the isothermal compression tests were carried out on Gleeble-3800 thermal simulator in the temperature range of 423−773 K and the deformation condition of 0.01–1.0 s−1 to study the cold temperature and hot deformation behavior of aluminum alloy AA6082. Considering the experimental error, the discussion based on friction and temperature correction is carried out. According to the modified data, based on the traditional Arrhenius constitutive model, a method combining the strain compensation Arrhenius model and PSO-BP neural network was proposed to describe the flow behavior of aluminum alloy AA6082. The prediction ability and stability of the model are evaluated by using the linear correlation coefficient(r), the average relative errors (AARE), the Root mean square errors(RMSE) and the relative errors (RE) in statistical analysis. The results show that the 3-10-8-1 double hidden layer neural network model based on PSO-BP has higher effect in predicting the flow characteristics of aging aluminum alloy AA6082 than that of Arrhenius model based on strain compensation. The linear correlation coefficient, mean relative error and root mean square error are 0.9996 %, 2.001 % and 1.665 MPa respectively.

Energy Prediction Model of PSO-BP Neural Network Three-dimensional Clusters based on Atomic Coordinates

Energy prediction for different cluster structures is the basis for finding and predicting the global optimal structure of clusters. The current methods for predicting the energy of the ground state structures of different clusters include theoretical prediction methods and optimized simplified potential energy function methods. The accuracy of the theoretical prediction method is high, but its calculation amount is too large. Therefore, this paper proposes a PSO-BP neural network three-dimensional cluster energy prediction model based on atomic coordinates, and uses different types of Euclidean distances between atoms as input variables, and the energy of clusters with different structures as output variables. Select gold cluster Au20 and boron cluster B45-part of the sample data as the training set to build the model, and predict the rest of the samples, and finally get: the prediction accuracy of the PSO-BP neural network model is higher than that of the traditional BP neural network model. The cluster energy prediction model is feasible.