Optimized Back Propagation Neural Network Research Articles

Disaster Risk Regionalization and Prediction of Corn Thrips Combined with Cloud Model: A Case Study of Shandong Province, China

Corn thrips do serious harm to the yield and quality of corn. In this paper, the Shandong Province of China was taken as the study area. Based on the data of the occurrence of corn thrips in Shandong Province, a risk regionalization model was established by using eight indicators under four categories of hazard, sensitivity, vulnerability and the disaster prevention and mitigation capacity of diseases and pests on a monthly time scale. Firstly, the cloud model was introduced to determine the weight of each indicator, and then the risk regionalization of the corn thrips disaster in Shandong Province was carried out using the weighted percentage method, the weighted comprehensive evaluation method and the natural disaster risk index method. Finally, combined with the collected data, the disaster prediction of corn thrip occurrence degree was realized based on multiple linear regression, genetic algorithm optimized back-propagation neural network and genetic algorithm optimized support vector machine methods. The results show that: (1) the risk of Corn thrips disaster is mainly concentrated in the central and western parts of Shandong Province. Heze City is a high-risk area. Liaocheng City, Dezhou City, Jinan City and Weifang City are relatively high-risk areas. (2) By comparing the prediction accuracy of the three models, it was determined that the genetic algorithm optimized support vector machine model has the best effect, with an average accuracy of 79.984%, which is 7.013% and 22.745% higher than that of the multiple linear regression and genetic algorithm optimized back-propagation neural network methods, respectively. The results of this study can provide a scientific basis for fine prevention of corn thrips in Shandong Province.

Prediction of tool wear in milling process based on BP neural network optimized by firefly algorithm

Long-time discontinuous contact is easy to cause tool wear during milling. To decrease the impact of severe wear on workpiece quality and processing efficiency, cutting tools should be replaced timely. Therefore, tool wear prediction is an important aspect in improving process efficiency, ensuring machining precision and realizing intelligent manufacturing. To boost the precision of online prediction of tool wear, this paper suggests a novel approach to monitor tool wear by optimizing backpropagation (BP) neural network via firefly algorithm (FA). Specifically, the progressive semi-soft threshold function is applied to the process of cutting force signal noise reduction, which reduces redundant signals and noise interference in the signal. Time-domain analysis, frequency-domain analysis, and wavelet packet decomposition are utilized, cutting force features are extracted, and Pearson correlation coefficient is used to sift out signal features that are highly connected to tool wear. The FA is used to improve the BP neural network's weights and thresholds. Through learning nonlinear mapping, relationship between tool flank wear and signal features is realized. A prediction model of tool wear of FA-BP is constructed. Milling experiments validate the prediction model in the milling process. The experimental outcomes confirm the precision and reliability of the method. In comparison to BP neural network, genetic algorithm optimized BP neural network and particle swarm optimization algorithm optimized BP neural network prediction method, it has a greater prediction accuracy and a stronger training impact, and has superior performance. The research results can give a theoretical foundation and technological assistance for predicting tool wear, which is crucial for improving workpiece quality, processing efficiency, and promoting intelligent development.

A wildfire occurrence risk model based on a back-propagation neural network-optimized genetic algorithm

To reduce the impact of wildfires on the operation of power systems, a back-propagation neural network (BPNN) model is used to evaluate the wildfire risk distribution after feature selection. Data from 14 types of wildfire-related features, including anthropogenic, geographical, and meteorological factors, were collected from public data websites and local departments. The weight ranking was calculated using filtering and wrapper methods to form five feature subsets. These are used as the input sets of the BPNN model training, and network parameters are optimized by genetic algorithm (GA). Finally, the optimal feature subset is chosen to establish the optimal BPNN model. With the optimal model, the prediction results are graded to draw a wildfire risk distribution map. Situated in medium-, high-, and very-high-risk zones are 90.26% of new fire incidents, indicating the applicability of the proposed BPNN model.

Internal leakage rate prediction and failure diagnosis of buried pipeline ball valve based on valve cavity pressure detection

— Ball valve is a key fluid control equipment used extensively in oil and gas pipelines. The online detection and failure diagnosis of the internal leakage of the ball valve is of great significance to ensure the safety operation of natural gas transmission pipelines. This paper proposes a prediction method of the internal leakage rate and a diagnosis method of the failure mode of the buried pipeline ball valve based on valve cavity pressure detection. Firstly, the valve cavity pressure signal generated by the internal leakage of the ball valve is detected by the pressure sensor, and the valve cavity pressure signal is denoised by wavelet threshold denoising. Then, the back propagation (BP) neural network has the disadvantage of unstable learning ability, so the BP neural network is optimized by chaos sparrow search optimization algorithm (CSSOA-BP). Finally, the prediction model of the ball valve internal leakage rate and the diagnosis model of the ball valve failure mode are established by using CSSOA-BP neural network and the characteristic parameters of the valve cavity pressure signal. To verify the performance of the prediction model and the diagnosis model of CSSOA-BP neural network, the predictive results and diagnostic results are compared with those of the sparrow search algorithm optimization BP (SSA-BP) neural network and BP neural network. The experimental results show that the maximum prediction error of CSSOA-BP neural network is the smallest, which is 13.6%. The accuracy of the diagnostic results of CSSOA-BP neural network is the highest, which is 83.3%. It indicates that the proposed method can achieve better predictive results of the ball valve internal leakage rate and more accurate diagnostic results of the ball valve failure mode.

Compensation of environmental parameters for optical detection of chemical oxygen demand

Chemical oxygen demand (COD) reflects the degree of water damage by organic pollutants, and is an important indicator for water environment protection and marine hydrological monitoring, so it is very important to accurately measure COD. However, changes in ambient temperature and atmospheric noise cause huge deviations in the precise measurement of COD by optical-based water quality detectors. The purpose of this research is to realize the accurate measurement of COD of the optical water quality detector by compensating the environmental parameters of the water quality detector. The compensation model established in this paper is a particle swarm optimization (PSO) back-propagation neural network (BPNN), which can compensate for temperature and filter out atmospheric noise, named back-propagation neural network of particle swarm optimization of filter out atmospheric noise (FAN-PSO-BPNN). FAN-PSO-BPNN reduced the maximum relative error by 92.51%, root mean square error by 91.64%, coefficient of variation by 91.74%, and the distance between the maximum and minimum prediction errors by 92.94% compared with BPNN in filtering out atmospheric noise interference and temperature compensation. The optimization scheme proposed in this paper for BPNN is crucial and effective, and the FAN-PSO-BPNN compensation model improves the anti-interference ability of the water quality detector, guarantees the accurate measurement of COD in seawater, and helps the water quality detector to monitor in seawater for a long time.

Process schemes of ethanol coupling to C4 olefins based on a genetic algorithm for back propagation neural network optimization

C4 olefin is an important feedstock for the chemical industry. Designing an effective and stable industrial process for preparing C4 olefin from renewable ethanol is crucial for further sustainable chemical production. In this study, a comprehensive evaluation system of an experimental scheme was constructed based on the Analytic Hierarchy Process/Entropy Weight Method-Technique for Order Preference by Similarity to Ideal Solution (AHP/EWM-TOPSIS) and Chemical production indicators. Using this evaluation system, a Back Propagation Neural Network (BPNN) based on a Genetic Algorithm (GA) was constructed after simulating C4 olefin production conditions using the Improved Mixed Congruential method. Subsequently, the production scheme with the highest evaluation score was determined when the temperature was not limited and when the temperature was lower than 350°C through a series of mathematical models. Overall, our mathematical models provide guidance for the commercial production of ethanol to butene and effectively reduce the risk of scaling up the chemical process to pilot or industrial scale.

Research on intelligent decision method of computer-aided manufacturing numerical control parameters based on model-based definition and back propagation neural networks

Aiming at the low efficiency and intelligence degree in the parameter setting of computer-aided manufacturing (CAM) numerical control (NC) programming caused by manual work, an intelligent decision-making method of CAM NC programming parameters based on model-based definition (MBD) is proposed. The hierarchical process model uses MBD technology to express the process flow and manage the process information. The tightness between process and NC machining is enhanced by sorting out NC machining steps and extracting their process information automatically. Then a back propagation (BP) neural network is used to mine the experience of NC programming in historical cases. Sparrow search algorithm (SSA) and other methods are used to optimize BP neural network’s initial weight and threshold, learning rate, and feedback process. A CAM parameter decision model based on BP neural network is constructed. Taking the connecting rod of a certain type of diesel engine as the experimental object, the method increases programming efficiency by 47%, with an accuracy rate of 80%. It is proved that the intelligent decision-making method proposed in this paper has practicability and feasibility.

Automated monitoring and evaluation of highway subgrade compaction quality using artificial neural networks

The compaction quality of a highway subgrade is influenced by the compaction parameters and soil properties. Current practice lacks reliable models to assess this multivariate situation. This paper describes a monitoring and evaluation method based on intelligent compaction (IC) and artificial neural networks (ANNs) to assess the compaction quality of a highway subgrade. Field compaction tests were conducted at various roller speeds and vibratory modes. The compactness was predicted using a particle-swarm-optimization optimized backpropagation neural network (PSO-BP-NN) model. The model was integrated into the IC system to automatically evaluate compaction quality. It also demonstrated the advantage of high prediction accuracy with multiple input variables of operation parameters and soil gradation. By efficiently controlling subgrade compaction, the model can be employed for engineering practice and facilitate IC applications.

Evaluation of fracture behavior of Yellow River ice based on three-point bending test and PSO-BP model

The fracture toughness of river ice is the primary mechanical characteristic that affects the dynamic behaviors of ice such as fracture, overlap, and accumulation. It can provide a scientific basis for the forecast of the break-up date of ice and the prevention and control of ice run. In this work, taking Yellow River ice as the research object, the fracture toughness under different types of ice (columnar ice and granular ice), temperatures (-2℃, −4℃, −8℃, and −10℃), and strain rates (10-6 s−1 ∼ 10-2s−1) was measured by three-point bending test. And the fracture toughness of the Yellow River ice was predicted and analyzed by back-propagation (BP) model and particle swarm optimization (PSO)-BP model. The experimental results exhibit that the fracture toughness of Yellow River ice mostly ranges from 30 kPa·m1/2 ∼ 130 kPa·m1/2. The fracture toughness of columnar ice and granular ice decreases with the increase of strain rates and temperatures, and the fracture property of columnar ice is better than that of granular ice. The probability of predicted values of train set and test set for BP model and PSO-BP model within 20 % error is 78.57 % and 85.71 % respectively. The analysis of the evaluation indicator presents the prediction performance of the model for the fracture toughness of Yellow River ice is promoted using PSO optimized BP neural network.

Preprocessing of Unstructured Data Using 2D Coiflet Wavelet-Based Optimized Back-Propagation Neural Network for Opinion Mining

Preprocessing of Unstructured Data Using 2D Coiflet Wavelet-Based Optimized Back-Propagation Neural Network for Opinion Mining

Pipeline damage identification based on an optimized back-propagation neural network improved by whale optimization algorithm

Pipeline damage identification based on an optimized back-propagation neural network improved by whale optimization algorithm

Prediction of Three-Dimensional Fractal Dimension of Hematite Flocs Based on Particle Swarm Optimization Optimized Back Propagation Neural Network

Prediction of Three-Dimensional Fractal Dimension of Hematite Flocs Based on Particle Swarm Optimization Optimized Back Propagation Neural Network

Corrosion life prediction of glass fiber reinforced plastics by optimized BP neural network

• The annealing particle swarm optimization algorithm is improved. The improved algorithm(im SAPSO) has stronger global search ability. • im SAPSO is used to replace the traditional neural network training method(most of these algorithms are based on gradient descent algorithm, so there will inevitably be local extremum problems), the effect is better. • The optimized neural network is used to predict the corrosion life prediction of glass fiber reinforced plastics. The output results of the network are in good agreement with the measured results, which proves the reliability of the algorithm. The retention rate of bending strength (RRBS) of glass fiber reinforced plastics (GFRP) in service under corrosive conditions is responsible for structural safety. However, it is very difficult to measure the RRBS by conventional measurement methods. In order to predict the service life of GFRP under corrosive conditions, a back-propagation (BP) neural network was establised. A series of experiments were carried out, considering three factors including temperature, time and corrosive medium concentration. 60 groups of RRBS were obtained and used as samples for neural network training. The network was optimized by improved simulated annealing particle swarm optimization algorithm (imSAPSO). The optimized network was then used to predict the other six groups of test data. The results show that the predicted values compare well to the measured, with maximum relative error of −0.615417% and minimum 0.015934%, and standard deviation of 0.00254567.

Regression prediction of hydrogen enriched compressed natural gas (HCNG) engine performance based on improved particle swarm optimization back propagation neural network method (IMPSO-BPNN)

Artificial neural network (ANN) methods have been rapidly developed and applied in solving nonlinear small sample problems. In this paper, an improved particle swarm algorithm optimized back propagation neural network (IMPSO-BPNN) method was proposed and used for the regression analysis and prediction of 20% (volume fraction) hydrogen enriched compressed natural gas (HCNG) engine performance. Meanwhile, various ANN and support vector machine (SVM) methods were also utilized for a comparative study. The experimental results show that the HCNG engine has the highest combustion efficiency, the maximum output torque and the minimum brake specific fuel consumption (BSFC) when operating at the maximum brake torque (MBT) timing, and the brake specific NOx (BSNOx) is also at a relatively low level. Through the comparison of multiple methods, the prediction accuracy and generalization ability of the IMPSO-BPNN model are the best overall. For example, the mean absolute percentage error (MAPE) of the optimal IMPSO-BPNN model (0.771%) is 5.85%, 12.62%, 17.96%, 7.57% and 7.88% less than that of the PSO-BPNN, GA-BPNN, BPNN, PSO-SVM and GA-SVM models (0.819%, 0.882%, 0.940%, 0.834% and 0.837%), respectively; and the correlation coefficient (R) is also higher than that of other models (0.99986). Secondly, the method shows visible superiority in the temporal dimension compared with the methods optimized by genetic algorithm and SVM method. For example, the CPU running time of the optimal IMPSO-BPNN model is reduced by 1773.32%, 149.74% and 1231.34% compared to that of the optimal GA-BPNN, PSO-SVM and GA-SVM models, respectively. Since the IMPSO-BPNN method is a flexible and general method, it is a new idea for the study of engine electronic control calibration tools.

Generating a Long-Term Spatiotemporally Continuous Melt Pond Fraction Dataset for Arctic Sea Ice Using an Artificial Neural Network and a Statistical-Based Temporal Filter

The melt pond fraction (MPF) is an important geophysical parameter of climate and the surface energy budget, and many MPF datasets have been generated from satellite observations. However, the reliability of these datasets suffers from short temporal spans and data gaps. To improve the temporal span and spatiotemporal continuity, we generated a long-term spatiotemporally continuous MPF dataset for Arctic sea ice, which is called the Northeast Normal University-melt pond fraction (NENU-MPF), from Moderate Resolution Imaging Spectroradiometer (MODIS) data. First, the non-linear relationship between the MODIS reflectance/geometries and the MPF was constructed using a genetic algorithm optimized back-propagation neural network (GA-BPNN) model. Then, the data gaps were filled and smoothed using a statistical-based temporal filter. The results show that the GA-BPNN model can provide accurate estimations of the MPF (R2 = 0.76, root mean square error (RMSE) = 0.05) and that the data gaps can be efficiently filled by the statistical-based temporal filter (RMSE = 0.047; bias = −0.022). The newly generated NENU-MPF dataset is consistent with the validation data and with published MPF datasets. Moreover, it has a longer temporal span and is much more spatiotemporally continuous; thus, it improves our knowledge of the long-term dynamics of the MPF over Arctic sea ice surfaces.

Design of feed rate monitoring system and estimation method for yield distribution information on combine harvester

Real-time monitoring of harvester feed rate is of great significance for guiding harvesting work and improving harvesting efficiency. In this study, a feed rate monitoring system is designed and developed. The system consists of a torque sensor for measuring the header power shaft torque, an angel sensor for measuring header height, a humidity sensor for measuring grains moisture content, a Global Navigation Satellite System receiver, and a vehicle industrial computer. Data, such as the moisture content of grains and the working position of the harvester, are collected by the system during harvest. The height of the header, the moisture content of grains, and the torque of the header power shaft are selected as the input quantity, and feed rate is selected as the output quantity. A calculation model for feed rate is established using a particle swarm optimization–back propagation neural network, and then real-time monitoring of feed rate is realized. On the basis of the monitoring of feed rate, an estimation method for yield distribution information is proposed. Through analyzing the rice samples in the experimental area, the method establishes a binary linear model between the header height, the grain moisture content, and the stem-to-grain ratio of the rice fed to the harvester. The model is combined with the monitoring results of feed rate to obtain the rice yield data fed to the harvester. Then, the rice yield data are matched with the positional information at the time of harvesting to obtain a yield distribution map of the harvested area. Field experiments are conducted in a rice-growing area in Northeast China, and the average relative error of the feed rate monitoring system is 7.69%. The field sample verification accuracy of the stem-to-grain ratio model is higher than 90%, and the correlation coefficient R2 is 0.93. The estimation accuracy of the yield distribution is higher than 90%, which provides a scientific basis for agricultural decision making.

An Apple Fungal Infection Detection Model Based on BPNN Optimized by Sparrow Search Algorithm.

To rapidly detect whether apples are infected by fungi, a portable electronic nose was used in this study to collect the gas information from apples, and the collected information was processed by smoothing filtering, data dimensionality reduction, and outlier removal. Following this, we utilized K-nearest neighbors (KNN), random forest (RF), support vector machine (SVM), a convolutional neural network (CNN), a back-propagation neural network (BPNN), a particle swarm optimization–back-propagation neural network (PSO-BPNN), a gray wolf optimization–backward propagation neural network (GWO-BPNN), and a sparrow search algorithm–backward propagation neural network (SSA-BPNN) model to discriminate apple samples, and adopted the 10-fold cross-validation method to evaluate the performance of each model. The results show that SSA can effectively optimize the performance of the BPNN, such that the recognition accuracy of the optimized SSA-BPNN model reaches 98.40%. This study provides an important reference value for the application of an electronic nose in the non-destructive and rapid detection of fungal infection in apples.

Prediction of Spherical Sheet Springback Based on a Sparrow-Search-Algorithm-Optimized BP Neural Network

Springback is an unavoidable problem in cold-forming processes and affects the efficiency and quality of the processing of outer sheets for ships. Therefore, effective control and prediction of sheet-forming springback is particularly important in the field of cold-bending processes. To this end, this paper presents research on cold-bending springback prediction based on a study of the multipoint cold-bending process combined with intelligent algorithms, as well as research on the multipoint cold-bending production of ship-hull plates. The forming process of spherical sheets was simulated by a finite element simulation. The amount of springback under different processes was studied, and the forming state and springback state were briefly analyzed. Then an in-depth study of machine learning was carried out, and the sparrow search algorithm (SSA) was introduced based on a back-propagation neural network (BPNN). The purpose of this integration was to prevent the BP neural network model from falling into local optimal solution problems. Then simulation data were obtained with the help of a simulation to build a backpropagation neural network prediction model, which was optimized based on the sparrow search algorithm, and training tests were conducted. Then the prediction results of the model were compared with the simulation data to verify that the prediction accuracy performance of the sparrow-search-algorithm-optimized BPNN model was improved. Finally, the prediction model based on the SSA–BPNN algorithm was compared with the prediction models of different algorithms, and the prediction results showed that SSA–BPNN outperformed other algorithms in prediction accuracy and speed; its prediction error was within 4%, which meets on-site processing requirements. The sparrow-search-algorithm-based optimization of BPNN was confirmed to have strong applicability in springback prediction.

Prediction of the Equivalent Steering Angle of a Front-Wheel, High-Clearance Paddy Field Management Machine

To solve the problem of poor steering consistency for each steering wheel of a four-wheel, independent-steering, high-clearance paddy field management machine, given that the true steering angle of the front wheel cannot be directly obtained through the left and right front wheels steering angle value, a BP (Back Propagation) neural network equivalent steering angle prediction method based on signal feature extraction is proposed in this paper, which can be used to obtain the equivalent steering angle of the front wheel. First, the kinematics model of the paddy field management machine was constructed with the prediction of the steering angle of the management machine as the object. The body was set up in two application environments of cement ground and paddy field, two moving speeds (25 cm/s, 50 cm/s) and three preset steering angles (0°, ±10°) to form six motion modes. The steering angle of the front wheel of the body, the three-axis acceleration of the body, and the angular velocity of the z-axis under twelve conditions were collected. Combining the collected data with the actual trajectory data of the differential BeiDou, the feasibility analysis of the equivalent steering angle prediction was carried out. With the aim of determining an appropriate combination of input factors for optimal equivalent steering angle prediction by the BP neural network, we used FFT (Fast Fourier Transform) and power spectrum conversion to extract and analyze the signal features of the airframe attitude data and obtain the frequency characteristics of the peak power point of the power spectrum, respectively. Through a factor combination test, the optimal BP neural network training factors were determined and, finally, we confirmed that the rotation angles of the front wheels and the z-axis angular velocity may be used as effective training factors. In our investigation on the test set, the mean square error of the equivalent steering angle was found to be less than 0.66°, demonstrating that our approach is effective for obtaining the true steering angle of the front wheel.

The Feasibility of Practical Vocational Education in Higher Education Institutions

Practical vocational education needs to lay stress on the integration of theoretical knowledge of common courses such as PE and other cultural courses with the professional knowledge of various high vocational courses. However, existing studies on practical vocational education emphasize more on theoretical research, while the practical and empirical research is slightly insufficient. For this reason, this paper attempts to analyze the feasibility of practical vocational education in higher education institutions. At first, the paper analyzed the influencing factors of the effect of practical vocational education on vocational college students, and adopted Partial Least Squares (PLS) regression to explain the independent variable and 17 dependent variables of the said effect and the relationship between each evaluation index and the corresponding evaluation criterion. Then, to verify the feasibility of practical vocational education in regional higher education institutions, this paper employed an optimized Back Propagation Neural Network (BPNN) to predict the effect of practical vocational education on vocational college students. At last, the experimental results proved the effectiveness of the constructed model and gave the conclusion of feasibility analysis.