Back Propagation Neural Network Algorithm Research Articles

Prediction of wood drying process based on artificial neural network

Taking the conventional drying process of Pinus sylvestris square wood with pith as the research material, based on the Back Propagation (BP) neural network algorithm, a model was constructed using the real-time online-measurement data. Softening treatment time and temperature, variable treatment time and temperature, initial moisture content of wood, and position of wood core and sapwood were used as model inputs. Wood drying rate and longitudinal cracking degree were used as outputs to indicate wood drying quality. The results showed that with a suitable model structure of 6-9-2 (input layer-hidden layer-output layer), the coefficient of determination R2 and mean square error of the test samples were 0.96, 0.99, and 0.00605, respectively, indicating that the neural network model has good generalization ability. Compared with the experimental value, the predicted value basically conforms to the change law and size of the experimental value, and the error distribution is approximately 2%. This shows that the BP neural network model can simulate the drying rate and longitudinal cracking degree in the drying process and realize the prediction of the drying process.

Research on the Heat Extraction Performance Optimization of Spiral Fin Coaxial Borehole Heat Exchanger Based on GA–BPNN–QLMPA

Geothermal energy, a renewable energy source with enormous reserves independent of the external environment, is essential for reducing carbon emissions. Spiral fin coaxial borehole heat exchanger (SFCBHE) is vital for geothermal energy extraction. Its heat extraction performance requires further improvements for efficient performance that consider the structural sizes and installation positions of the SFCBHE and the nonlinear coupling with respect to several factors. The heat extraction performance of SFCBHE is optimized using a combination of genetic algorithm–back-propagation neural network (GA–BPNN) and the Q-learning-based marine predator algorithm (QLMPA). This study analyzes and compares the effects of geothermal energy extraction of smooth pipe TY-1, structure before optimization TY-2, and optimized structure TY-3. Following optimization with GA–BPNN–QLMPA, the heat extraction performance of TY-3 is enhanced by 30.8% and 23.6%, respectively. The temperature of maximum extraction is improved by 26.8 K and 24.0 K, respectively. The power of maximum heat extraction is increased by 148.2% and 109.5%, respectively. The optimization method can quickly and accurately determine the heat extraction performance for different structural sizes and installation positions of the SFCBHE. These findings are crucial for developing high-performance SFCBHE and efficiently using geothermal energy.

Resistive Sensing of Seed Cotton Moisture Regain Based on Pressure Compensation.

The measurement of seed cotton moisture regain (MR) during harvesting operations is an open and challenging problem. In this study, a new method for resistive sensing of seed cotton MR measurement based on pressure compensation is proposed. First, an experimental platform was designed. After that, the change of cotton bale parameters during the cotton picker packaging process was simulated through the experimental platform, and the correlations among the compression volume, compression density, contact pressure, and conductivity of seed cotton were analyzed. Then, support vector regression (SVR), random forest (RF), and a backpropagation neural network (BPNN) were employed to build seed cotton MR prediction models. Finally, the performance of the method was evaluated through the experimental platform test. The results showed that there was a weak correlation between contact pressure and compression volume, while there was a significant correlation (p < 0.01) between contact pressure and compression density. Moreover, the nonlinear mathematical models exhibited better fitting performance than the linear mathematical models in describing the relationships among compression density, contact pressure, and conductivity. The comparative analysis results of the three MR prediction models showed that the BPNN algorithm had the highest prediction accuracy, with a coefficient of determination (R2) of 0.986 and a root mean square error (RMSE) of 0.204%. The mean RMSE and mean coefficient of variation (CV) of the performance evaluation test results were 0.20% and 2.22%, respectively. Therefore, the method proposed in this study is reliable. In addition, the study will provide a technical reference for the accurate and rapid measurement of seed cotton MR during harvesting operations.

A High-Resolution Spatial Distribution-Based Integration Machine Learning Algorithm for Urban Fire Risk Assessment: A Case Study in Chengdu, China

The development and functional perfection of urban areas have led to increasingly severe fire risks in recent decades. Previous urban fire risk assessment methods relied on subjective judgment, rough data collection, simple linear statistical methods, etc. These drawbacks can lead to low robustness of evaluation and inadequate generalization ability. To resolve these problems, this paper selects the indicator and regression models based on the high-resolution data of the spatial distribution characteristics of Longquanyi distinct in Chengdu, China. and proposes an integrated machine learning algorithm for fire risk assessment. Firstly, the kernel density analysis is used to map the fourteen urban characteristics related to fire risks. The contributions of these indicators (characteristics) to fire risk and its corresponding index are determined by Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and eXtreme Gradient Boosting (XGBoost). Then, the spatial correlation of fire risks is determined through Moran’s I, and the spatial distribution pattern of indicator weights is clarified through the raster coefficient space analysis. Finally, with these selected indicators, we test the regression performance with a backpropagation neural network (BPNN) algorithm and a geographically weighted regression (GWR) model. The results indicate that numerical variables are more suitable than dummy variables for estimating micro-scale fire risks. The main factors with a high contribution are all numerical variables, including roads, gas pipelines, GDP, hazardous chemical enterprises, petrol and charging stations, cultural heritage protection units, assembly occupancies, and high-rise buildings. The machine learning algorithm integrating RF and BPNN shows the best performance (R2 = 0.97), followed by the RF-GWR integrated algorithm (R2 = 0.87). Compared with previous methods, this algorithm reduces the subjectivity of the traditional assessment models and shows the ability to automatically obtain the key indicators of urban fire risks. Hence, this new approach provides us with a more robust tool for assessing the future fire safety level in urban areas.

Short-Term Power Demand Forecasting Using Blockchain-Based Neural Networks Models

With the rapid development of blockchain technology, blockchain-based neural network short-term power demand forecasting has become a research hot spot in the power industry. This paper aims to combine neural network algorithms with blockchain technology to establish a trustworthy and efficient short-term demand forecasting model. By leveraging the distributed ledger and immutability features of blockchain, we ensure the security and reliability of power demand data. Meanwhile, short-term power demand forecasting research using neural networks has the potential to increase the stability of the power system and offer opportunities for improved operations. In this paper, the root mean-square-error model evaluation indicator was used to compare the back propagation (BP) neural network algorithm and the traditional forecasting algorithm. The evaluation was performed on the randomly selected five household power datasets. The results show that, by comparing the long short-term memory network (LSTM) model with the BP neural network model, it was determined that the average prediction impact increases by about 25.7% under stable power demand. The short-term power prediction model of the BP neural network has the average error values more than two times lower than the traditional prediction model. It was shown that the use of the BP neural network algorithm and blockchain could increase the accuracy of short-term power demand forecasting, allowing the neural network-based algorithm to be implemented and taken into account in the research on short-term power demand forecasting.

Dual carbon oriented optimization method for manufacturing industry chain based on BP neural network and clonal selection algorithm

Global economic growth and increasing carbon emissions pose significant challenges to managing and optimizing industry chains. Against the backdrop of carbon peak and carbon neutrality goals, manufacturing enterprises forgo economic efficiency to achieve low-carbon goals. Therefore, balancing the economic efficiency and carbon emissions of the entire industrial chain has become a vital issue for manufacturing enterprises. Based on the existing research, "a dual carbon industrial chain" is proposed in this study to reveal the characteristics of the industrial chain under the dual carbon goals. A collaborative optimization model under the "dual carbon industrial chain" was constructed by combining complex networks, back-propagation (BP) neural networks, and clonal selection algorithm to optimize the economic efficiency and carbon emissions of the entire industrial chain. Using a chip industry chain as an example, an optimization solution from the perspective of the overall industry chain is proposed. The results show that the proposed method can optimize the economic benefits and carbon emissions of the entire industrial chain, which has specific theoretical and practical significance for developing modern industrial chains with a dual carbon background.

Modeling of soluble solid content of PE-packaged blueberries based on near-infrared spectroscopy with back propagation neural network and partial least squares (BP-PLS) algorithm.

Blueberries are a nutritious and popular berry worldwide. The physical and chemical properties of blueberries constantly change through the cycle of the supply chain (from harvest to sale). The purpose of this study was to develop a rapid method for detecting the properties of packaged blueberries based on near-infrared (NIR) spectroscopy. NIR was applied to quantitatively determine the soluble solid content (SSC) of polyethylene (PE)-packaged blueberries. An orthogonal partial least squares discriminant analysis model was established to show the correlation between spectral data and the measured SSC. Multiplicative scattering correction, standard normal variable, Savitzky-Golay convolution first derivative, and normalization (Normalize) were used for spectra preprocessing. Uninformative variables elimination, competitive adaptive reweighted sampling, and iteratively retaining informative variables were jointly used for wavelength optimization. NIR-based SSC prediction models for unpacked blueberries and PE-packaged blueberries were developed using partial least squares (PLS). The prediction model for PE-packaged samples (RP 2 =0.876, root mean square error of prediction [RMSEP]=0.632) had less precision than the model for unpacked samples (RP 2 =0.953, RMSEP=0.611). To reduce the effect of PE, the back propagation (BP) neural network and PLS were combined into the BP-PLS algorithm based on the residual learning algorithm. The model of BP-PLS (RP 2 =0.947, RMSEP=0.414) was successfully developed to improve the prediction accuracy of SSC for PE-packaged blueberries. The results suggested a promising way of using the BP-PLS method in tandem with NIR for the rapid detection of the SSC of PE-packaged blueberries. PRACTICAL APPLICATION: Most of the NIR-based research used unpacked blueberries as samples, while the use of packaged blueberries would provide researchers with a better understanding of the crucial factors at different phases of the blueberry supply chain (from harvest to sale). To meet market demands and minimize losses, NIR spectroscopy has been proven to be a rapid and nondestructive method for the determination of the SSC of PE-packaged blueberries. This study provides an effective method for monitoring the properties of blueberries in the entire supply chain.

Predicting Chinese stock prices using convertible bond: an evidence-based neural network approach

PurposeThe primary objective of this study is to investigate whether the inclusion of convertible bond prices as important inputs into artificial neural networks can lead to improved accuracy in predicting Chinese stock prices. This novel approach aims to uncover the latent potential inherent in convertible bond dynamics, ultimately resulting in enhanced precision when forecasting stock prices.Design/methodology/approachThe authors employed two machine learning models, namely the backpropagation neural network (BPNN) model and the extreme learning machine neural networks (ELMNN) model, on empirical Chinese financial time series data.FindingsThe results showed that the convertible bond price had a strong predictive power for low-market-value stocks but not for high-market-value stocks. The BPNN algorithm performed better than the ELMNN algorithm in predicting stock prices using the convertible bond price as an input indicator for low-market-value stocks. In contrast, ELMNN showed a significant decrease in prediction accuracy when the convertible bond price was added.Originality/valueThis study represents the initial endeavor to integrate convertible bond data into both the BPNN model and the ELMNN model for the purpose of predicting Chinese stock prices.

Small-signal behavioral-level modeling of InP HBT based on SO-BP neural network

In this paper, Snake Optimizer-Back Propagation (SO-BP) neural network algorithm has been proposed to model the small-signal characteristic of InP heterojunction bipolar transistors (HBTs). The SO algorithm can effectively improve the problem of local optima encountered by the BP neural network algorithm. The InP HBT small-signal models have been constructed by using Back Propagation (BP) algorithm, the Genetic Algorithm-Back Propagation (GA-BP) algorithm and the Snake Optimizer-Back Propagation (SO-BP) algorithm. The experimental results verify that the proposed SO-BP neural network algorithm model has the lowest error rate between simulated and measured data. Hence, the SO-BP algorithm is a more effective approach for modeling the small-signal characteristic of InP HBT. The accurate small-signal behavioral-level model of HBT has significant reference value for the application of microwave and millimeter-wave integrated circuits (MMICs).

Comprehensive Calculation Method of Semantic Similarity of Transport Infrastructure Ontology Concept Based on SHO-BP Algorithm

Semantic information interaction plays an important role in transportation infrastructure modeling and management. To ensure semantic consistency during information exchange and data integration, ontology technology is commonly employed to measure the semantic relevance between concepts. Ontology semantic similarity accurately expresses relationships among various concepts in the domain, and when combined with Building Information Modeling (BIM) technology, it improves the efficiency of information transmission and management in construction. However, the complex structure, diverse components, and strong attribute diversity of transportation infrastructure pose challenges for analysis and computation, leading to limited precision in existing ontology semantic similarity methods. Aimed at these issues, this paper proposes a transport infrastructure ontology concept semantic similarity measurement model based on the Back Propagation (BP) neural network algorithm improved by the Spotted Hyena Optimizer (SHO-BP). Firstly, a semantic network for transportation infrastructure is established, and an ontology-based semantic similarity calculation model is constructed with three approaches, including Edge-Counting method, Feature-based method, and Information-Content method. Then, the SHO-BP algorithm is employed to comprehensively weight the three similarity measure approaches above. Finally, using bridge BIM models as examples, the semantic similarity of transportation infrastructure concepts involved in the BIM models are computed based on the weighted model derived from the aforementioned processes. The experiments demonstrate that the SHO-BP algorithm achieves a higher Pearson correlation coefficient than other algorithms for the comprehensive semantic similarity results in the field of transportation infrastructure. This improvement effectively enhances the accuracy of ontology semantic similarity calculation, and it is conducive to the sharing and integration of BIM information in different systems.

Optimization of tetrastigma hemsleyanum extraction process based on GA-BPNN model and analysis of its antioxidant effect

Tetrastigma hemsleyanum (Tetrastigma hemsleyanum Diels et Gilg) is a valuable traditional Chinese medicine with various applications. In this study, we aimed to optimize the extraction process for the total extraction yield of five flavonoid components, namely kaempferol, quercetin, rutin, kaempferol-3-O-rutinoside, and astragalin from the Tetrastigma hemsleyanum root (THR), and explore its potential molecular mechanisms in treating oxidative diseases as well as antioxidant activity. To achieve these objectives, we employed the genetic algorithm-back propagation neural network (GA-BPNN), the Box-Behnken design (BBD) with 4-factors and 3-levels to establish the optimal ethanol extraction process for the total extraction yield of the 5 components. Using public databases, the “component core targets-disease core target genes” networks were built, as well as molecular docking. Furthermore, DPPH was used to examine the antioxidant activity of the extracts obtained from THR under the optimal extraction process. The experimental value of the total extraction yield of the 5 components achieved a maximum of 788.12 mg/kg when the ethanol concentration was 73%, the solid-liquid ratio was 26 g/mL, and the ultrasonic duration was 30 min, and the ultrasonic temperature was 76 °C. When docked with protein molecules such as 6Y8I, quercetin, and other components received moderate to high scores. When the total concentration of the 5 components was 3.033 μg/mL, the DPPH radical scavenging rate was 89.81%. Compared with the BBD method, the GA-BPNN method is more efficient and reliable for optimizing the extraction process of active ingredients in THR because of its good data-fitting ability.

Analysis of the Use of Artificial Neural Network Models in Predicting Bitcoin Prices

Bitcoin is one of the fastest-growing digital currencies or cryptocurrencies in the world. However, the highly volatile Bitcoin price poses a very extreme risk for traders investing in cryptocurrencies, especially Bitcoin. To anticipate these risks, a prediction system is needed to predict the fluctuations in cryptocurrency prices. Artificial Neural Network (ANN) is a relatively new model discovered and can solve many complex problems because the way it works mimics human nerve cells. ANN has the advantage of being able to describe both linear and non-linear models with a fairly wide range. This research aims to determine the best performance and level of accuracy of the ANN model using the Back-Propagation Neural Network (BPNN) algorithm in predicting Bitcoin prices. This study uses Bitcoin price data for the period 2020 to 2023 taken from the CoinDesk market. The results of this study indicate that the ANN model produces the best performance in the form of four input nodes, 12 hidden nodes, and one output node (4-12-1) with an accuracy rate of around 3.0617175%.

Research on the Response Characteristics and Identification of Infrasound Signals in the Jialongcuo Ice Avalanche, Tibet

Due to the inability of remote sensing satellites to monitor avalanches in real time, this study focuses on the glaciers in the rear edge of Jialongcuo, Tibet, and uses infrasound sensors to conduct real-time monitoring of ice avalanches. The following conclusions are drawn: (1) In terms of waveform, compared to background noise, ice avalanche events have a slight left deviation and a slightly steep shape; compared to wind, rain, and floods events, ice avalanche events have less obvious kurtosis and skewness. (2) In terms of frequency distribution, the infrasound frequency generated by ice avalanche events is mainly distributed in the range of 1.5 Hz to 9.5 Hz; compared to other events, ice avalanche events differ some in frequency characteristics. (3) The model based on information entropy and marginal spectral frequency distribution characteristics of infrasound have higher accuracy in signal classification and recognition, as they can better represent the differences between infrasound signals of different events than other features. (4) Compared with the K-nearest neighbor algorithm and classification tree algorithm, the support vector machine and BP (Back Propagation) neural network algorithm are more suitable for identifying infrasound signals in the Jialongcuo ice avalanche. The research results can provide theoretical support for the application of infrasound-based ice avalanche monitoring technology.

The prediction of Chongqing's GDP based on the LASSO method and chaotic whale group algorithm–back propagation neural network–ARIMA model

Accurate GDP forecasts are vital for strategic decision-making and effective macroeconomic policies. In this study, we propose an innovative approach for Chongqing's GDP prediction, combining the LASSO method with the CWOA—BP–ARIMA model. Through meticulous feature selection based on Pearson correlation and Lasso regression, we identify key economic indicators linked to Chongqing's GDP. These indicators serve as inputs for the optimized CWOA–BP–ARIMA model, demonstrating its superiority over Random Forest, MLP, GA–BP, and CWOA–BP models. The CWOA–BP–ARIMA model achieves a remarkable 95% reduction in MAE and a significant 94.2% reduction in RMSE compared to Random Forest. Furthermore, it shows substantial reductions of 80.6% in MAE and 77.8% in RMSE compared to MLP, along with considerable reductions of 77.3% in MAE and 75% in RMSE compared to GA–BP. Moreover, compared to its own CWOA–BP counterpart, the model attains an impressive 30.7% reduction in MAE and a 20.46% reduction in RMSE. These results underscore the model's predictive accuracy and robustness, establishing it as a reliable tool for economic planning and decision-making. Additionally, our study calculates GDP prediction intervals at different confidence levels, further enhancing forecasting accuracy. The research uncovers a close relationship between GDP and key indicators, providing valuable insights for policy formulation. Based on the predictions, Chongqing's GDP is projected to experience positive growth, reaching 298,880 thousand yuan in 2022, 322,990 thousand yuan in 2023, and 342,730 thousand yuan in 2024. These projections equip decision-makers with essential information to formulate effective policies aligned with economic trends. Overall, our study provides valuable knowledge and tools for strategic decision-making and macroeconomic policy formulation, showcasing the exceptional performance of the CWOA–BP–ARIMA model in GDP prediction.

Physically-Data Driven Approach for Predicting Formation Leakage Pressure: A Dual-Drive Method

Formation leak-off pressure, which sets the upper limit of the safe drilling fluid density window, is crucial for preventing wellbore accidents and ensuring safe and efficient drilling operations. The paper thoroughly examines models of drilling physics alongside artificial intelligence techniques. The study introduces a dual-driven method for predicting reservoir pore pressure by integrating long short-term memory (LSTM) and backpropagation (BP) neural networks, where the core component is the LSTM-BP neural network model. The input data for the LSTM-BP model include wellbore diameter, formation density, sonic time, natural gamma, mud content, and pore pressure. The study demonstrates the practical application of the method using two vertical wells in Block M, employing the M-1 well for training and the M-2 well for validation. Two distinct input layer configurations are devised for the LSTM-BP model to evaluate the influence of formation density on prediction accuracy. Notably, Scheme 2 omits formation density as a variable in contrast to Scheme 1. The study’s results indicate that, for input layer configurations corresponding to Scenario 1 and Scenario 2, the LSTM-BP model exhibits relative error ranges of (−2.467%, 2.510%) and (−6.141%, 5.201%) on the test set, respectively. In Scenario 1, the model achieves mean squared error (MSE), mean absolute error (MAE), and R-squared (R2) values of 0.000229935, 0.011198329, and 0.92178272, respectively, on the test set. Conversely, for Scenario 2, the model demonstrates a substantial escalation of 992.393% and 240.674% in MSE and MAE, respectively, compared to Scenario 1; however, R2 diminishes by 66.920%. Utilizing the trained LSTM-BP model, predictions for formation lost pressure in Well M-2 reveal linear correlation coefficients of 0.8173 and 0.6451 corresponding to Scenario 1 and Scenario 2, respectively. These findings imply that the predictions from the Scenario 1 model demonstrate stronger alignment with results derived from formulaic calculations. These observations remain consistent for both the BP neural network algorithm and the random forest algorithm. The aforementioned research results not only highlight the elevated predictive precision of the LSTM-BP model for intelligent prediction of formation lost pressure, a product of this study, thereby furnishing valuable data points to enhance the security of drilling operations in Block M, but also underscore the necessity of deliberating both physical relevance and data correlation during the selection of input layer variables.

Modeling of Precipitation Prediction Based on Causal Analysis and Machine Learning

The factors influencing precipitation in western China are quite complex, which increases the difficulty in determining accurate predictors. Hence, this paper models the monthly measured precipitation data from 240 meteorological stations in mainland China and the precipitation data from the European Centre for Medium-Range Weather Forecasts and the National Climate Centre and employs 88 atmospheric circulation indices to develop a precipitation prediction scheme. Specifically, a high-quality grid-point field is created by fusing and revising the precipitation data from multiple sources. This field is combined with the Empirical Orthogonal Function decomposition and the causal information flow. Next, the best predictors are screened through Empirical Orthogonal Function decomposition and causal information flow, and a data-driven precipitation prediction model is established using a Back Propagation Neural Network and a Random Forest algorithm to conduct the 1-month, 3-month, and 6-month precipitation predictions. The results show that: The machine learning-based precipitation prediction model has high accuracy and is generally able to predict the precipitation trend in the western region better. The Random Forest algorithm significantly outperforms the Back Propagation Neural Network algorithm in the prediction of the three starting times, and the prediction ability of both models gradually decreases as the starting time increases. Compared with the 2022 flood season prediction scores of the Institute of Atmospheric Sciences of the Chinese Academy of Sciences, the model improves the prediction of 1-month and 3-month precipitation in the western region and provides a new idea for the short-term climate prediction of precipitation in western China.

Data Reconstruction of Sea Surface Temperature in Indonesia’s Fish Management Area 713 (IFMA-713) Using Machine Learning

The IFMA-713 in Indonesia is water that has dynamic of temperature changes due to interactions with the Pacific Ocean and the surrounding. Sea surface temperature data can be obtained by measuring with satellite imagery. However, satellite imagery measurements of sea surface temperature can be incomplete due to cloud cover. In this study, a machine learning method was used to reconstruct sea surface temperature data using a backpropagation neural network algorithm. The data used in this research is data captured with MODIS Satellite. Then, the reconstruction of sea surface temperature data is carried with four scenarios with missing data percentages: empty data, zero values, average values at the point of data collection, and Indonesia’s average sea surface temperature. Accurate results were obtained in reconstructing sea surface temperature where the scenarios had a positive correlation. The most accurate scenarios for reconstructing sea surface temperature data with missing data were those in which the empty data was filled with average values at the point of data collection or Indonesia’s average sea surface temperature.

Determination of polysaccharide content in shiitake mushroom beverage by NIR spectroscopy combined with machine learning: A comparative analysis

A comprehensive comparative analysis of the performance of near-infrared (NIR) spectroscopy combined with various chemometrics and machine learning methods was performed to quantitatively determine polysaccharide content in shiitake mushroom beverages. First, Savitzky-Golay (SG) and orthogonal signal correction (OSC) methods were used for spectral pretreatment. Then, to simplify the model and enhance the generalization performance, variable selection (VS) methods including selectivity ratio (sRatio), variable importance in projection (VIP), recursive partial least square (rPLS), synergy interval partial least square (si-PLS), genetic algorithm (GA), and successive projection algorithm (SPA) were used to select the feature wavelengths. Finally, multiple linear regression (MLR), principal component regression (PCR), partial least squares regression (PLSR), support vector regression (SVR), back propagation neural network (BPNN) and extreme gradient boosting (XGBoost) regression algorithms were used for the multivariate quantitative analysis of NIR data. The results show that different combinations of pretreatment, VS and modelling methods lead to different prediction performances. Except for XGBoost, almost all linear and non-linear models showed satisfying prediction performance with their Rp2 > 0.95. Of these, SG-VS-BPNN models exhibited the best performance with their Rp2 ≥ 0.98. Therefore, NIR combined with machine learning can provide an intelligent, nondestructive and rapid quantitation of polysaccharide content in beverages. Data will be made available on request.

Research on the Prediction Model of Engine Output Torque and Real-Time Estimation of the Road Rolling Resistance Coefficient in Tracked Vehicles.

Road parameter identification is of great significance for the active safety control of tracked vehicles and the improvement of vehicle driving safety. In this study, a method for establishing a prediction model of the engine output torques in tracked vehicles based on vehicle driving data was proposed, and the road rolling resistance coefficient f was further estimated using the model. First, the driving data from the tracked vehicle were collected and then screened by setting the driving conditions of the tracked vehicle. Then, the mapping relationship between the engine torque Te, the engine speed ne, and the accelerator pedal position β was obtained by a genetic algorithm-backpropagation (GA-BP) neural network algorithm, and an engine output torque prediction model was established. Finally, based on the vehicle longitudinal dynamics model, the recursive least squares (RLS) algorithm was used to estimate the f. The experimental results showed that when the driving state of the tracked vehicle satisfied the set driving conditions, the engine output torque prediction model could predict the engine output torque T^e in real time based on the changes in the ne and β, and then the RLS algorithm was used to estimate the road rolling resistance coefficient f^. The average coefficient of determination R of the T^e was 0.91, and the estimation accuracy of the f^ was 98.421%. This method could adequately meet the requirements for engine output torque prediction and real-time estimation of the road rolling resistance coefficient during tracked vehicle driving.

Retraction: Digital image processing technology under backpropagation neural network and K-Means Clustering algorithm on nitrogen utilization rate of Chinese cabbages.

Retraction: Digital image processing technology under backpropagation neural network and K-Means Clustering algorithm on nitrogen utilization rate of Chinese cabbages.