PSO-BP Neural Network Model Research Articles

A Productivity Prediction Method of Fracture-Vuggy Reservoirs Based on the PSO-BP Neural Network

Reservoir productivity prediction is a key component of oil and gas field development, and the rapid and accurate evaluation of reservoir productivity plays an important role in evaluating oil field development potential and improving oil field development efficiency. Fracture-vuggy reservoirs are characterized by strong heterogeneity, complex distribution, and irregular development, causing great difficulties in the efficient prediction of fracture-vuggy reservoirs’ productivity. Therefore, a PSO-BP fracture-vuggy reservoir productivity prediction model optimized by feature optimization was proposed in this paper. The Chatterjee correlation coefficient was used to select the appropriate combination of seismic attributes as the input of the prediction model, and we applied the PSO-BP model to predict oil wells’ production in a typical fracture-vuggy reservoir area of Tahe Oilfield, China, with the selected seismic attributes and compared the accuracy with that provided by the BP neural network, linear support vector machine, and multiple linear regression. The prediction results using the four models based on the test set showed that compared with the other three models, the MSE of the PSO-BP model increased by 23% to 62%, the RMSE increased by 12 to 38 percent, the MAE increased by 18 to 44 percent, the SSE increased by 23 to 62 percent, and the R-square value increased by 2 to 13 percent. This comparison proves that the PSO-BP neural network model proposed in this paper is suitable for the productivity prediction of fracture-vuggy reservoirs and has better performance, which is of guiding significance for the development and production of fracture-vuggy reservoirs.

Macro-microscopic study on the crack resistance of polyester fiber asphalt mixture under dry-wet cycling and neural network prediction

Asphalt mixture is a composite material with a complex multiphase dispersed system, and its macroscopic mechanical behavior is inherently related to the microstructure characteristics. To investigate the low-temperature crack resistance degradation law of polyester fiber asphalt mixture under different dry-wet cycling conditions, six polyester fiber contents (0%, 0.3%, 0.35%, 0.4%, 0.45%, and 0.5%) were dry-blended into SMA-13 asphalt mixture to prepare fiber-reinforced SCB specimens with pre-notches. Semi-circular bending tests were conducted to test the crack resistance performance of the fiber asphalt mixture after 0, 2, 4, 6, and 8 dry-wet cycles. The experimental results show that the crack resistance performance of specimens with different fiber contents decreases with the increase of dry-wet cycling times, and the influence of salt-dry-wet coupling is greater than that of water-dry-wet coupling. Under the same conditions, the crack resistance index (CRI) increases first and then decreases with the increase of polyester fiber content, reaching its maximum at a polyester fiber content of 0.4%. When the fiber is added at 0.5%, the agglomeration of polyester fibers restricts the low-temperature performance of the specimen. In addition, the DIC technology is used to analyze the trend of horizontal strain variation of specimens after different dry-wet cycles. The results indicate that with the increase of the cycle period, the strain concentration area becomes more apparent, and in the destruction stage, the full-field horizontal strain Exx in the crack concentration zone gradually increases, while the crack resistance decreases. Finally, addressing the limitations of traditional BP neural networks in solving nonlinear problems, a particle swarm optimization algorithm is proposed to optimize the BP neural network, and a PSO-BP neural network model is constructed. Through the analysis of evaluation indicators, it is found that the PSO-BP neural network has improved generalization ability compared to the traditional BP neural network model, and overfitting is reduced, making it an effective tool for predicting the low-temperature performance of polyester fiber asphalt mixtures.

Optimization of Aluminum Alloy Formwork Geometry Parameters Based on a PSO-BP Neural Network

To assist in addressing the problem where an aluminum alloy formwork (AAF) deforms more greatly under the action of lateral pressure and therefore does not meet the requirements of plaster-free engineering, we propose a method for determining the geometric parameters of this formwork based on a PSO algorithm and BP neural network with ABAQUS as the platform. The influence of six geometric parameters of the formwork on the maximum deflection value of the panel under the action of lateral pressure is studied using finite element analysis. The maximum deflection value of the panel is used as the index, and the influence of each factor is analyzed with an orthogonal test, and a set of optimal geometric parameters is obtained via extreme difference analysis and analysis of variance. The sample data are obtained via finite element simulation, and the PSO-BP neural network model is established using the six factors of the orthogonal test as input values and the maximum deflection of the panel as the output value, and the optimal geometric parameters are optimized using the PSO algorithm. The results indicate that the maximum deflection for the panel in the orthogonal scheme is 1.446 mm. The PSO-BP neural network prediction model demonstrates greater accuracy and a 31.74% reduction in running time compared to the BP neural network prediction model. The optimized PSO-BP neural network prediction model scheme reveals a maximum panel deflection of 1.296 mm, a 10.37% decrease compared to the orthogonal solution. These findings offer technical guidance and a foundation for optimizing AAF designs, presenting practical applications.

Prediction of SO2 and NOx in sintering flue gas based on PSO-BP neural network model

ABSTRACT In the long process of iron and steel, the sintering process has the largest amount of flue gas emissions, many types of pollutants and high concentrations. The source control of SO2 and NOx in sintering flue gas through digital technology has become a new emission reduction technology. In this study, the BP neural network model (BP-NN) is optimized by using the particle swarm algorithm (PSO) to form the PSO-BPNN model, which effectively improves the characteristics of BP-NN with slow convergence speed and easily falls into local minima, and improves the learning ability and generalization. The test results show that the PSO-BP-NN algorithm not only has fast convergence speed and high prediction accuracy, but also has smaller training and inspection errors. In addition, this model combines process theory and feature engineering selection of parameters, which effectively improves the accuracy of the model and the interpretability of the results based on the linkage of process knowledge, and has certain analytical significance for the source management and post-treatment of sintered flue gas.

Traffic safety assessment and prediction under different lighting service states in road tunnels

The lighting service state of road tunnels has an important impact on traffic safety. To assess and predict traffic safety in road tunnels under different lighting service states, a traffic safety assessment system of road tunnels was innovatively established by using visual recognition clarity data from 300 field experiments, which uses the proposed traffic safety factor to assess the traffic safety of road tunnels under different lighting service states. Then, the Particle Swarm Optimization (PSO) algorithm is used to optimize the Back Propagation (BP) neural network, and a new traffic safety intelligent prediction method in road tunnels was constructed to predict the traffic safety factor under different lighting service states. The results of the study show that there is a negative correlation between simulated vehicle speed and visual clarity and a positive correlation between lighting attenuation and visual clarity. There is a negative correlation between the number of luminaire failures and the visual recognition clarity. When the lighting attenuation is below 70% or four and above luminaires fail, the lighting service states of road tunnels can seriously threaten traffic safety. The PSO-BP neural network model can accurately predict the traffic safety factor. The critical value of the traffic safety factor is 0.5965. This means that the lighting service state poses a threat to traffic safety in road tunnels when the traffic safety factor is lower than this value. The results of the study can provide the basis for developing a safe and low-carbon tunnel lighting maintenance program.

Retracted: Prediction Model for Driver Reaction Time Based on PSO-BP Neural Network Model

Retracted: Prediction Model for Driver Reaction Time Based on PSO-BP Neural Network Model

Research on Urban Greenway Alignment Selection Based on Multisource Data

At present, people’s growing need for a beautiful ecological environment needs to be met. As a high-quality ecological product, greenways help improve the living environment and promote green and coordinated development. How to scientifically and rationally plan greenway routes and expand the green ecological network has become an important issue for the sustainable development of high-density built-up areas. Therefore, this study determines the suitability evaluation system of urban greenway alignment selection based on spatial constructiveness, greenway accessibility, resource attractiveness and public demand through factor analysis. Taking Xixi Wetland to South Lake in Yuhang District as an example, combining urban multi-source data and GIS spatial analysis methods, all routes suitable for greenway construction in the study area were determined, and four excellent greenway routes were initially identified through manual correction. Subsequently, the PSO-BP neural network model was trained using 172 greenway samples to obtain a greenway alignment suitability evaluation model with an accuracy of more than 96%, which was used for subsequent greenway alignment optimization. The above four preliminary greenway routes are substituted into the PSO-BP model to obtain the optimal greenway routes in the study area. The results show that there are 17 indicators in 4 levels and 8 dimensions in the urban greenway route selection potential model, among which two indicators, public cycling and running demand, have a greater weight and should be highly considered in the route selection assessment. The optimal planned route for the greenway start from Guangfu Temple entrance of Xixi Wetland, follow the roadside green belt and Yuhangtang River greenway to South Lake. The study obtained the best planning route for the greenway from Xixi Wetland to South Lake in Yuhang District, which is a new practice of landscape garden planning and design supported by multisource data and new technologies, with a view to providing a reference for the preferential selection of urban greenway routes in similar high-density built-up areas.

Prediction of the Height of Water Flowing Fractured Zone Based on the MPSO-BP Neural Network Model

The height of the water flowing fractured zone (HWFFZ) is the key parameter for safe coal mining under water bodies. In order to improve the accuracy of prediction model of the HWFFZ, the grey relational analysis (GRA) is used to quantitatively analyze the influencing factors of the HWFFZ, the main influencing factors of the HWFFZ under the condition of comprehensive mechanized mining and comprehensive top coal caving mining are selected scientifically, and these five factors that include mining depth, mining thickness, inclined length of working face, hard rock lithology proportion coefficient, and coal seam dip angle are selected as the discriminant indexes for predicting the HWFFZ. Nonlinear inertia weight adjustment, linear adjustments of local acceleration factor and global acceleration factor, and Gaussian mutation operation are introduced into particle swarm optimization (PSO) algorithm to improve the performance of PSO algorithm. The modified particle swarm optimization (MPSO) algorithm is used to optimize the initial weights and thresholds of back propagation (BP) neural network, and the prediction model of the HWFFZ based on MPSO-BP neural network algorithm is established according to the measured data. The prediction results are compared with those of PSO-BP neural network model and empirical formulas, the results show that the prediction accuracy of MPSO-BP neural network prediction model is higher than that of PSO-BP neural network prediction model and empirical formulas, and its prediction mean relative error (MRE) is smallest. The model also has good application effect and certain guiding significance for actual production.

Prediction Model for Driver Reaction Time Based on PSO-BP Neural Network Model

A critical element of accident reconstruction technology is the driver reaction time, which is also a key aspect of evaluating driver takeover time in autonomous driving. There are various factors affecting driver reaction time, including driver psychological factors, nondriving tasks, external environment, and other reasons. It is necessary to record and predict the driver reaction time to support the takeover time in the degraded takeover study because this paper is designed to conduct a degraded takeover study in the human-machine codriving stage with safety of the intended functionality. Therefore, a model based on PSO-BP neural network algorithm to predict driver reaction time is developed. A wavelet transform algorithm is used to denoise the signal first in order to improve the convergence speed and prediction accuracy of the model. Meanwhile, the BP neural network prediction model based on the PSO is established to optimize the weights and thresholds of the BP neural network to achieve the prediction of the driver reaction time. A total of six main feature parameters of driver’s HRV in the time and frequency domains were selected as input indicators and substituted into the input signal of PSO-BP neural network model for training and testing. The prediction results obtained from the PSO-BP neural network model were compared with that of the BP neural network prediction, and it demonstrated that the prediction results obtained in this paper have smaller error values, verifying the reasonableness and validity of the model.

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.

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.

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.

State-of-charge Estimation of Lithium-ion Batteries Based on PSO-BP Neural Network

The state of charge (SOC) of lithium-ion battery is a variable and cannot be measured directly by sensors. Therefore, accurate estimation of battery state of charge is the key to ensure the safe and reliable operation of battery management system (BMS) and reduce the required battery cost. In the research of estimating the state of charge of lithium-ion batteries, the initial setting of the weights and thresholds of the BP neural network easy to falls into the local minimum problem, which makes the SOC estimation insufficiently accurate. Therefore, a method of SOC estimation of lithium-ion battery based on particle swarm optimization (PSO) and BP neural network is proposed in this paper. Taking lithium manganese battery (LiMn₂O₄) as the object, use the multi-physics simulation platform COMSOL to conduct charging and discharging experiments on it, and collect the relevant performance parameters of the battery. Under the condition of constant temperature and constant current, the SOC of the battery is inferred according to the voltage and discharge rate. Building a PSO-BP neural network model with voltage and discharge rate as input and battery SOC as output. The performance of SOC estimation is evaluated from the aspects of overall correlation, training time and robustness. It is compared with the estimation method based on BP neural network. The simulation results show that the absolute error of the estimation method based on PSO-BP neural network is 2.68%, which is 3.18% higher than that of BP neural network, and the accuracy is higher. The proposed method has more advantages.

Research on hot deformation behavior of Zr-4 alloy based on PSO-BP artificial neural network

The high-temperature deformation behavior of Zr-4 alloy was investigated by using isothermal compression tests performed on the Gleeble-3500 thermomechanical simulator in the range of temperatures 750–1000 °C with strain rates of 0.001–10s−1. Based on the above hot deformation conditions, the flow stresses are predicted efficiently and accurately by the PSO-BP artificial neural network model. The architecture of the network includes two hidden layers; the first hidden layer includes 15 neurons and the second 10 neurons. And the established model was further evaluated in terms of the correlation coefficient, absolute error, and average absolute relative error. The results show the measured and predicted flow stresses have a good agreement, which indicates that the PSO-BP neural network model can be widely used to analyze and predict the hot deformation behaviors at different experimental conditions. As revealed by micrographs, the phenomenon of spheroidization appears at a strain rate of 0.001s−1 below the transformation temperature of the β phase, while the lath-shaped microstructure is transformed into a microstructure that the grain is nearly equiaxed at the same condition mentioned above. However, the microstructure becomes uneven at a high strain rate above the transformation temperature of the β phase.

Learned Prediction of Compressive Strength of GGBFS Concrete Using Hybrid Artificial Neural Network Models.

A new hybrid intelligent model was developed for estimating the compressive strength (CS) of ground granulated blast furnace slag (GGBFS) concrete, and the synergistic benefits of the hybrid algorithm as compared with a single algorithm were verified. While using the collected 269 data from previous experimental studies, artificial neural network (ANN) models with three different learning algorithms namely back-propagation (BP), particle swarm optimization (PSO), and new hybrid PSO-BP algorithms, were constructed and the performance of the models was evaluated with regard to the prediction accuracy, efficiency, and stability through a threefold procedure. It was found that the PSO-BP neural network model was superior to the simple ANNs that were trained by a single algorithm and it is suitable for predicting the CS of GGBFS concrete.

Prediction model of PSO-BP neural network on coliform amount in special food.

Special food safety supervision by means of intelligent models and methods is of great significance for the health of local people and tourists. Models like BP neural network have the problems of low accuracy and poor robustness in food safety prediction. So, firstly, the principal component analysis was used to extract the key factors that influenced the amount of coliform communities, which was applied to reduce the dimension of this model as the input variable of BP neural network. Secondly, both the particle swarm optimization (PSO) and BP neural network were implemented to optimize initial weights and threshold to obtain the optimal parameter, and a model was constructed to predict the amount of coliform bacteria in Dai Special Snacks, Sa pie, based on PSO-BP neural network model. Finally, the predicted value of the model is verified. The results show that MSE is 0.0097, MAPE is 0.3198 and MAE is 0.0079, respectively. It was clear that PSO-BP model was better accuracy and robustness. That means, this model can effectively predict the amount of coliform. The research has important guiding significance for the quality and the production of Sa pie.

Cognitive Competence Improvement for Autonomous Vehicles: A Lane Change Identification Model for Distant Preceding Vehicles

The motion status recognition of the preceding vehicle in a long-distance region is a requirement for autonomous vehicles to make appropriate decisions and increase their comprehension of the environment. At present, the lane change behavior of the leading vehicle at a short distance is detected using stereo cameras and LiDAR. However, the short detection distance (about 100 m) does not meet the requirements of high-speed driving of autonomous vehicles on expressways; this is a fundamental problem limiting the development of autonomous vehicles exhibiting human-like behavior. In this paper, a comprehensive model consisting of a back-propagation (BP) neural network model optimized by a particle swarm optimization (PSO) algorithm, and a continuous identification model is developed based on the results of naturalistic on-road experiments using millimeter-wave radar data. By considering different time-to-lane crossings (TLCs), the PSO-BP neural network model is trained using real vehicle lane change data and implemented when the TLC of the leading vehicle is longer than 1.0 s. In contrast, when the TLC is less than 1 s, the continuous recognition model of the TLC is used. By comparison with the BP neural network model, the recognition accuracy rate of the proposed model is increased from 80% to 87% after the PSO optimization for a time window of 1.0 s; these results meet the recognition requirements of the autonomous driving systems for distant targets. The findings of this paper improve the cognitive competence and safety of autonomous driving systems.

Design of Intrusion Detection System for Internet of Things Based on Improved BP Neural Network

With the advent of global 5G networks, the Internet of Things will no longer be limited by network speed and traffic. With the large-scale application of the Internet of Things, people pay more and more attention to the security of the Internet of Things. Once the Internet of Things system suffers from malicious attacks, not only the serious loss of information will lead to the paralysis of the Internet of Things equipment. Aiming at the security problem of the Internet of Things, this paper puts forward the LM-BP neural network model. The LM-BP neural network model is applied to an intrusion detection system, and the intrusion detection flow under LM-BP algorithm is given. LM algorithm has the characteristics of fast optimization speed and strong robustness and uses this characteristic to optimize the weight threshold of traditional BP neural network. Through establishing LM-BP neural network classifier, KDD CUP 99 intrusion detection data set is imported into an LM-BP neural network classifier, and the best results are obtained through continuous training. Finally, the experimental simulation results show that this model has higher detection rate and lower false alarm rate than the traditional BP neural network model and PSO-BP neural network model for DOS, R2L, U2L, and Probing, thus this modified model has certain promotion value.

PSO-BP Neural Network Grade Prediction Model Based on Bagging Ensemble Learning

Performance prediction is a very basic but important part of the application of educational big data, and it plays an important role in education management. For performance prediction, this paper innovatively integrates Bagging ensemble learning, PSO particle swarm optimization and neural network algorithm, proposes a Bagging-based PSO-BP neural network model, and uses real data to verify the model data. In the low-dimensional performance prediction problem has got very good results.