Thresholds Of Back Propagation Neural Network Research Articles

An Improved Self-Adaptive Flower Pollination-Backpropagation Neural Network for the State of Charge Estimation of Lithium-Ion Batteries with Output Sliding Average Window Strategy

With the rapid development of electric vehicles and green energy sources, the use of backpropagation neural network (BPNN) to precisely estimate the state of charge (SOC) in lithium-ion batteries has become a popular research topic. However, traditionally BPNN has low prediction accuracy and large output fluctuations. To address the shortcomings of BPNN, self-adaptive flower pollination algorithm (SFPA) was proposed to optimize the initial weights and thresholds of BPNN, and an output sliding average window (OSAW) strategy is proposed to smooth SOC outputs in this research, which SOC estimation method is named SFPA-BP-OSAW. In addition, the performance of the newly proposed method is compared with other common related algorithms under different working conditions to verify the effectiveness of SFPA-BP-OSAW. The experimental results show that the mean absolute error of SFPA-BP-OSAW is 0.771% and 0.897%, and the root mean square error is 0.236% and 0.37%, respectively, under HPPC and BBDST working conditions. Experimental data and error analysis show that the method proposed in this paper has fast convergence, high prediction accuracy, and curve smoothness.

Application of QGA-BP Neural Network in Debt Risk Assessment of Government Platforms

How to correctly understand the existence of local government debt, study its risk classification and impact, give full play to the “dual nature” of debt with a full-caliber indicator system, and avoid debt risks to the greatest extent. That is the research direction of this article. In order to improve the accuracy and efficiency of risk assessment and effectively reduce the debt risk of government platform companies, a risk assessment method based on optimized back-propagation (BP) neural network is proposed. First, the method uses quantum genetic algorithm (quantum genetic algorithm, QGA) to adjust and determine the initial weight and threshold of BP neural network and realize the optimization of BP neural network model parameter setting. Then, the QGA-BP debt risk assessment of government platforms is verified that it performs well in the debt risk prediction of government platform companies, and its prediction accuracy and prediction speed are improved.

Effective fault module localization in substation critical equipment: an improved ant colony optimization and back propagation neural network approach

AbstractThe rapid development of substations has increased the demand for accurate and fast fault prediction systems. In order to achieve rapid localization and autonomous decision‐making of fault modules and types in substations, the article proposes a fault autonomous localization algorithm based on improved ant colony optimization (IACO) and back propagation neural network (BPNN). The fault data of the substation secondary equipment for training and testing the BPNN model is based on the actual operating equipment of the substation, which can significantly improve the reliability of the model results. In addition, the IACO is used to globally optimize the weights and thresholds of BPNN, and the number of hidden layer nodes in BPNN was analyzed to further improve the accuracy of the established fault prediction algorithm. The test results show that the fault prediction accuracy of the BPNN model optimized by IACO is 93.67%, which is significantly improved compared to the traditional BPNN and BPNN with ant colony optimization method (with an accuracy of 82.98% and 91.04%). The above results effectively demonstrate the high accuracy and effectiveness of the established prediction algorithm in processing data and locating faults, which can improve the maintenance and operational efficiency of substations.

Assessing project portfolio risk via an enhanced GA-BPNN combined with PCA

Assessing project portfolio risk (PPR) is essential for organizations to grasp the overall risk levels of project portfolios (PPs) and realize PPR mitigation. However, current research is inadequate to effectively assess PPR, which brings challenges to managing PPR. In this context, the purpose of this study is to develop a PPR assessment model via an enhanced backpropagation neural network (BPNN). First, PPR assessment criteria considering project interdependencies are determined. Second, fuzzy logic is used to obtain original data for assessment criteria. Principal component analysis (PCA) is then employed to reduce the dimensionality of assessment criteria and derive the input and output of BPNN. Third, an improved genetic algorithm (IGA) is designed to optimize the initial weights and thresholds of BPNN. On this basis, the PCA-IGA-BPNN assessment model is constructed, followed by training and testing, possessing a test accuracy of 98.6%. Finally, comparison experiments are conducted from both internal and external perspectives. For internal comparison, the proposed model yields less mean absolute percentage error (MAPE), mean square error (MSE), and root mean square error (RMSE) than PCA-GA-BPNN, IGA-BPNN, PCA-BPNN and BPNN and offers the largest convergence speed (γ). As for external comparison, the presented model produces lower MAPE, MSE, and RMSE than Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Neighbor (KNN) and has the largest coefficient of determination (R2). Results indicate that the established model performs more satisfactorily in assessing PPR. This research enriches PPR assessment methods and provides managers with a useful tool to evaluate PPR.

Application of GA-BPNN on estimating the flow rate of a centrifugal pump

Pumps consume nearly 8% of global electricity as the essential equipment for liquid transportation. A practical method for improving centrifugal pump energy efficiency is accurately predicting and controlling the pump operation status. However, current estimation methods for sensorless flow rate prediction have a significant error at low flow rate conditions. This study adds valve opening as the estimation model input variable, including motor shaft power and speed, to form a back-propagation neural network (BPNN) on an asynchronous motor-driven multistage centrifugal pump. By optimizing the initial weights and thresholds of BPNN, a GA-BPNN model was proposed to improve the prediction accuracy by using a genetic algorithm (GA). The results indicate that, with the addition of the valve opening as an input variable, the accuracy of BPNN-VO and GA-BPNN prediction improves significantly more than BPNN-NVO. Furthermore, the GA-BPNN model produces a significantly lower mean square error (MSE) and root mean square error (RMSE) than the original BPNN model. According to the experimental comparison and analysis, the absolute error of GA-BPNN between predicted flow rate and measured flow rate is less than 0.3 m3/h, the average relative error is less than 2%, and the relative error of low flow rate is less than 5%. This GA-BPNN estimation model significantly improves the accuracy of flow rate prediction, especially at small flow rates, and extends the scope of centrifugal pumps’ monitoring and control technology without flow sensors.

Prediction of milk protein content based on improved sparrow search algorithm and optimized back propagation neural network

The quality of milk is largely determined by the protein content. The feasibility of predicting the protein content of milk by hyperspectral image has attracted more attentions from researchers for minor detection cost and high efficiency. In this paper, a prediction modeling method based on improved sparrow search algorithm (SSA) and optimized back propagation (BP) neural network is proposed, in which sine chaotic map is introduced to initialize the population position to improve the optimization performance of SSA. In the experiment, hyperspectral images of each kind of milk were collected by visible/near infrared hyperspectral imaging system to acquire hyperspectral data, then the spectral data were pretreated by Savitzky–Golay smoothing, and the competitive adaptive reweighted sampling combined with successive projections algorithm to select 13 characteristic bands. Subsequently, the spectral data corresponding to the characteristic bands are used as the input of back propagation neural network, optimized by the improved sparrow search algorithm for the initial weight and threshold of BP neural network, to establish three prediction models(BP model, the BP model based on SSA optimization and the BP model based on improved SSA optimization).Experimental results demonstrate that the BP model based on improved SSA optimization has better fitting ability and higher prediction accuracy for milk protein content. This research provides algorithm support and theoretical basis for the rapid nondestructive detection of milk protein content based on BP neural network.

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.

Prediction of Low-Energy Building Energy Consumption Based on Genetic BP Algorithm

Combined with the energy consumption data of individual buildings in the logistics group of Yangtze University, the analysis model scheme of energy consumption of individual buildings in the university is studied by using Back Propagation (BP) neural network to solve nonlinear problems and have the ability of global approximation and generalization. By analyzing the influence of different uses, different building surfaces and different energy-saving schemes on the change of building energy consumption, the grey correlation method is used to determine the main influencing factors affecting each building energy consumption, including uses, building surfaces and energy-saving schemes, which are used as the input of the model and the building energy consumption as the output of the model, so as to establish the building energy consumption analysis model based on BP neural network. However, in practical application, BP neural network has the defects of slow convergence and easy to fall into local minima. In view of this, this paper uses genetic algorithm to optimize the weight and threshold of BP neural network, completes the improvement of various building energy consumption analysis models, and realizes the qualitative analysis of building energy consumption. The model verification results show that the viscosity of the building energy consumption analysis model based on genetic algorithm improved BP neural network algorithm (GABP) in this paper is relatively high, which is more accurate than the results of the traditional BP neural network model, and the relative error of the analysis model is reduced from 11.56% to 8.13%, which proves that the GABP can be better suitable for the study of school building energy consumption analysis model, It is applied to the prediction of building energy consumption, which lays a foundation for the realization of carbon neutralization in the South expansion plan of Yangtze University.

Automatic Recognition of Geomagnetic Suitability Areas for Path Planning of Autonomous Underwater Vehicle

Currently, integrated navigation systems with the inertial navigation system (INS)/geomagnetic navigation system (GNS) have been widely used in underwater navigation of autonomous underwater vehicle (AUV). Restricting AUV to navigate in the geomagnetic suitability areas (GSA) as far as possible can effectively improve the accuracy of integrated navigation systems. In order to improve the classification accuracy of GSA, a new optimal classification method based on principal component analysis (PCA) and improved back propagation (BP) neural network is proposed. PCA is used to extract the independent characteristic parameters containing the main components. Then, considering similarity coefficient, the initial weights and thresholds of BP neural network is optimized by improved adaptive genetic algorithm (IAGA). Finally, the correspondence between the geomagnetic characteristic parameters and matching performance is established based on PCA and improved adaptive genetic algorithm and back propagation (IAGA-BP) neural network for the automatic recognition of GSA. Simulated experiments based on PCA and IAGA-BP neural network shows high classification accuracy and reliability in the GSA selection. The method could provide important support for AUV path planning, which is an effective guarantee for AUV high precision and long voyage autonomous navigation.

Research on prediction model of thermal and moisture comfort of underwear based on principal component analysis and Genetic Algorithm–Back Propagation neural network

Abstract In order to improve the efficiency and accuracy of thermal and moisture comfort prediction of underwear, a new prediction model is designed by using principal component analysis method to reduce the dimension of related variables and eliminate the multi-collinearity relationship between variables, and then inputting the converted variables into genetic algorithm (GA) and BP neural network. In order to avoid the problems of slow convergence speed and easy falling into local minimum of Back Propagation (BP) neural network, this paper adopted GA to optimize the weights and thresholds of BP neural network, and utilized MATLAB software to program, and established the prediction models of BP neural network and GA–BP neural network. To verify the superiority of the model, the predicted result of GA–BP, PCA–BP and BP are compared with GA–BP neural network. The results show that PCA could improve the accuracy and adaptability of GA–BP neural network for thermal and moisture comfort prediction. PCA–GA–BP model is obviously superior to GA–BP, PCA–BP, BP, SVM and K-means prediction models, which could accurately predict thermal and moisture comfort of underwear. The model has better accuracy prediction and simpler structure.

Study on prediction model of diesel engine with regulated two-stage turbocharging system based on hybrid genetic algorithm-particle swarm optimization method at different altitudes

ABSTRACT Aiming at the problem of complicated mechanism modeling for regulated two-stage turbocharging (RTST) system of diesel engine, a hybrid genetic algorithm–particle swarm optimization (GA-PSO) method was applied to optimize back propagation neural network (BPNN) performance prediction model of diesel engine. First, using a high-altitude (low-pressure) simulation test bench for diesel engines, a full-load performance test of RTST diesel engine at different altitudes was carried out, and the regulation rules of diesel engine intake characteristics and two-stage turbocharger (TST) matching characteristics with altitude were obtained. Based on the test data, the BPNN is used to establish the boost pressure prediction model of the RTST system of the diesel engine. For the problem of slow convergence and low prediction accuracy of the BPNN, adaptive improvement measures such as momentum and learning rate are introduced, and the GA-PSO optimizes the weights and thresholds of BPNN. The performance comparison shows that hybrid GA-PSO is superior to particle swarm optimization (PSO) and genetic algorithm (GA) in terms of evolution speed and convergence accuracy. Genetic algorithm–particle swarm optimization–back propagation neural network (GA-PSO-BPNN) model has higher prediction accuracy and higher accuracy than particle swarm optimization–back propagation neural network (PSO-BPNN) model and genetic algorithm–back propagation neural network (GA-BPNN) model. Compared with the original model, the root-mean-square error of the model is reduced by 50%, the mean error percentage (MEP) is reduced by 38%, and the sum of the squared errors is reduced by 79%. The performance verification of the GA-PSO-BPNN model shows that the MEP of the model prediction results is 5.5%, and the GA-PSO-BPNN model has higher prediction accuracy.

Application of Improved BP Neural Network in Information Fusion Kalman Filter

Based on improved back propagation (BP) neural network, information fusion state estimation problem for multi-sensor system is considered. Firstly, particle swarm optimization, search dynamic learning rate and additional momentum method are introduced to train the initial weights and thresholds of BP neural network. Then, the improved neural network is used to optimize the estimated value of Kalman filter. Finally, the sate estimators are fused by weighting matrices. A simulation example verifies the effectiveness of the proposed algorithm.

Rolling Bearing Fault Prediction Method Based on QPSO-BP Neural Network and Dempster–Shafer Evidence Theory

To effectively predict the rolling bearing fault under different working conditions, a rolling bearing fault prediction method based on quantum particle swarm optimization (QPSO) backpropagation (BP) neural network and Dempster–Shafer evidence theory is proposed. First, the original vibration signals of rolling bearing are decomposed by three-layer wavelet packet, and the eigenvectors of different states of rolling bearing are constructed as input data of BP neural network. Second, the optimal number of hidden-layer nodes of BP neural network is automatically found by the dichotomy method to improve the efficiency of selecting the number of hidden-layer nodes. Third, the initial weights and thresholds of BP neural network are optimized by QPSO algorithm, which can improve the convergence speed and classification accuracy of BP neural network. Finally, the fault classification results of multiple QPSO-BP neural networks are fused by Dempster–Shafer evidence theory, and the final rolling bearing fault prediction model is obtained. The experiments demonstrate that different types of rolling bearing fault can be effectively and efficiently predicted under various working conditions.

Parameter Estimation for PMSM based on a Back Propagation Neural Network Optimized by Chaotic Artificial Fish Swarm Algorithm

Permanent Magnet Synchronous Motor(PMSM) control system with strong nonlinearity makes it difficult to accurately identify motor parameters such as stator winding, dq axis inductance, and rotor flux linkage. Aiming at the premature convergence of traditional Back Propagation Neural Network(BPNN) in PMSM motor parameter identification, a new method of PMSM motor parameter identification is proposed. It uses Chaotic Artificial Fish Swarm Algorithm(CAFSA) to optimize the initial weights and thresholds of BPNN, and then strengthens training by BPNN algorithm. Thus, the global optimal network parameters are obtained by using the global optimization of CAFSA and the local search ability of BPNN. The simulation results and experimental data show that the initial value sensitivity of the network model optimized by CAFS-BPNN Algorithm is weak, the parameter setting is robust, and the system stability is good under complex conditions. Compared with other intelligent algorithms, such as RSL and PSO, CAFS-BPNNA has high identification accuracy and fast convergence speed for PMSM motor parameters.

Research on the Prediction Method of Ultimate Bearing Capacity of PBL Based on IAGA-BPNN Algorithm

In order to better predict ultimate bearing capacity of perfobond leiste shear connection (PBL), the six specimens were designed for push-out test, and the prediction models were built based on an Improved Adaptive Genetic Algorithm (IAGA) and Back Propagation neural network (BPNN) algorithm. With the finite element model established, it was found that the effects of different parameters on the ultimate bearing capacity of PBL vary greatly if using a single parameter method. The calculation results showed that transverse reinforcement diameter, hole diameter of steel plate, the thickness of steel plate and the strength grade of concrete were the four key factors affecting the ultimate bearing capacity of PBL. In order to overcome the disadvantages of BPNN, such as slow convergence speed and easy to fall into local optimization, an improved adaptive genetic algorithm is used to optimize the initial weights and thresholds of BPNN. The comparison shows that the algorithm is superior to the standard genetic algorithm and other heuristic algorithms, in terms of convergence speed, global search ability and robustness. The IAGA-BPNN prediction model was established. Using the experimental data obtained from both the fatigue test and the references as samples to train the prediction model, the results show that the IAGA-BPNN algorithm proposed in this article can accurately predict the ultimate bearing capacity of PBL, with an average error of 1.69%. The comprehensive sensitivity analysis (CSA) method adopts to explore the relative contribution of each key factors and the interaction between the key factors, and the analysis results show that the ultimate bearing capacity of PBL increases significantly with the increase of the thickness of steel plate and hole diameter of steel plate. The accuracy and stability were better than formulas for calculating the ultimate bearing capacity and the BP neural network prediction algorithm.

A Hybrid Forecasting Model for the Velocity of Hybrid Robotic Fish Based on Back-Propagation Neural Network With Genetic Algorithm Optimization

Marine unmanned vehicle is a novel robot widely used in ocean observation, and its accurate control is of significance to their path planning. We want to find a method to predict the velocity and course of this robot, which can help us realize the accurate control of it. The paper proposed a promising type of hybrid robotic fish (HRF), which can realize two kinds of motion modes on the sea surface. Firstly, the configuration and dynamic model of the HRF were analyzed elaborately. Then, to realize accurate velocity prediction under two kinds of motion modes of HRF, the influence factors are presented in a complex marine environment. Based on the influence factors to its maneuverability, such as wind or wave parameters, a velocity prediction algorithm based on back-propagation neural network (BPNN) was introduced. However, BPNN has the disadvantages of extended learning and training time, easily falling into local optimum. Then we found that genetic algorithm (GA), which is a kind of evolutionary algorithm, is suitable for our problem. Therefore, the accuracy and efficiency of the prediction algorithm were improved by adopting the genetic algorithm to optimize the weight and threshold of BPNN. Taking the experimental data from the pool test, the back-propagation neural network with genetic algorithm (GA-BPNN) forecasting model was established. Besides, the other prediction methods were compared and evaluated under the same assessment criterion to validate the proposed forecasting model. The experimental results demonstrate that the GA-BPNN model has higher accuracy and efficiency compared with other prediction algorithms, which verifies the feasibility of the velocity prediction model for hybrid robotic fish in complex ocean environments.

Prediction of Survival Time of Patients With Esophageal Squamous Cell Carcinoma Based on Univariate Analysis and ASSA-BP Neural Network

Esophageal squamous cell carcinoma (ESCC) is one of the most common malignant tumors in the world. In order to find out the influencing factors, univariate Cox regression analysis is used to analyze the blood indexes to screen out the factors affecting the survival or death of patients. Spearman and Pearson correlation analysis can verify whether screening factors are related to survival. The survival curves and progression-free survival curves of 5 factors are given after the threshold is obtained by receiver operating characteristics (ROC). In order to optimize the survival accuracy of patients with ESCC, in view of the low convergence accuracy and easy to fall into local optimization of back propagation (BP) prediction network, the improved adaptive salp swarm algorithm (ASSA), genetic algorithm (GA) and back propagation (BP) neural network are combined. The initial weight and threshold of BP neural network are determined, and the ASSA-BP prediction model and GA-BP prediction model are established. In order to show the reliability and accuracy of the new model in a large range, the ASSA-BP model, GA-BP model and BP model are evaluated respectively. The ASSA-BP model is more effective in predicting the survival time of patients with ESCC, and shortens the training time and improves the prediction accuracy.

Prediction of coal and gas outburst: A method based on the BP neural network optimized by GASA

With the development of computer science, many machine learning algorithms such as the back propagation (BP) neural network algorithm have been applied to the prediction of coal and gas outburst. However, the BP neural network algorithm can easily fall into the local minimum and has a low convergence rate. In this study, the genetic algorithm (GA) with strong global search ability and the simulated annealing algorithm (SA) with strong local search ability were integrated into a new GASA algorithm. This algorithm, which boasts strong spatial search ability and serves to optimize the initial weight and threshold of BP neural network, succeeds in solving the problem of easily falling into the local minimum. Furthermore, an improved GASA-BP prediction model was established by introducing an adaptive learning rate into the original BP neural network algorithm. The model is applied to coal and gas outburst prediction in Weicun Coal Mine in Jiaozuo City, Henan Province, China. Experimental results show that compared with the BP and GA-BP models, the GASA-BP model can predict coal and gas outburst accurately and quickly.

Nuclear accident source term estimation using Kernel Principal Component Analysis, Particle Swarm Optimization, and Backpropagation Neural Networks

Rapid estimation of the release rate of source items after a nuclear accident is very important for nuclear emergency and decision making. A source term estimation method, based on the Backpropagation Neural Network (BPNN), was developed. Kernel Principal Component Analysis (KPCA) is used to reduce the dimension of the input parameters, which can accelerate the training of the neural network. Particle Swarm Optimization (PSO) is used to optimize weights and thresholds of BPNN, so that the neural network can better find the global optimal value, avoid falling into the local minimum. The large amount of data required for neural network training is generated using InterRAS software, the model constructed demonstrates the feasibility of this method. The proposed method can estimate the release rate of I-131 after half an hour of release, which is helpful to the emergency response, or provide an initial value or priori information for other methods.

Improved Social Emotion Optimization Algorithm for Short-Term Traffic Flow Forecasting Based on Back-Propagation Neural Network

The back-propagation neural network (BPNN) is a well-known multi-layer feed-forward neural network which is trained by the error reverse propagation algorithm. It is very suitable for the complex of short-term traffic flow forecasting; however, BPNN is easy to fall into local optimum and slow convergence. In order to overcome these deficiencies, a new approach called social emotion optimization algorithm (SEOA) is proposed in this paper to optimize the linked weights and thresholds of BPNN. Each individual in SEOA represents a BPNN. The availability of the proposed forecasting models is proved with the actual traffic flow data of the 2nd Ring Road of Beijing. Experiment of results show that the forecasting accuracy of SEOA is improved obviously as compared with the accuracy of particle swarm optimization back-propagation (PSOBP) and simulated annealing particle swarm optimization back-propagation (SAPSOBP) models. Furthermore, since SEOA does not respond to the negative feedback information, Metropolis rule is proposed to give consideration to both positive and negative feedback information and diversify the adjustment methods. The modified BPNN model, in comparison with social emotion optimization back-propagation (SEOBP) model, is more advantageous to search the global optimal solution. The accuracy of Metropolis rule social emotion optimization back-propagation (MRSEOBP) model is improved about 19.54% as compared with that of SEOBP model in predicting the dramatically changing data.