Back-propagation Network Research Articles

Mathematical formulation of the machine learning backpropagation network and regression modelling of the chemical stability and thermal properties of PLA/HKUST-1 fabricated porous membranes

Mathematical formulation of the machine learning backpropagation network and regression modelling of the chemical stability and thermal properties of PLA/HKUST-1 fabricated porous membranes

Enhanced Short-Term Temperature Prediction of Seasonally Frozen Soil Subgrades Using the NARX Neural Network

Accurate prediction of subgrade temperatures in seasonally frozen regions is crucial for understanding thermal states, frost heave phenomena, stability, and other critical characteristics. This study employs a nonlinear autoregressive with exogenous input (NARX) network to predict short-term subgrade temperatures in the Golmud-Nagqu section of China’s National Highway 109. The methodology involves preprocessing subgrade monitoring data, including temperature, water content, and frost heave, followed by developing a temperature prediction model. This tailored NARX neural network, compared to the traditional BP neural network, integrates feedback and delay mechanisms for monitoring data, offering superior memory and dynamic response capabilities. The precision of the NARX model is assessed with the backpropagation (BP) network, indicating that the NARX neural network significantly outperforms the BP model in both precision and stability for temperature prediction in seasonally frozen subgrades. These findings suggest that the NARX model is a valuable tool for accurately predicting subgrade temperatures in seasonally frozen regions, offering significant insights for practical engineering applications.

Intelligent Pressure Monitoring Method of BP Neural Network Optimized by Genetic Algorithm: A Case Study of X Well Area in Yinggehai Basin

While drilling formation pressure monitoring is an important basis for ensuring drilling safety and oil and gas discovery, the calculation of existing pressure monitoring methods is complicated and the accuracy is difficult to improve. Taking the actual well data of well area X in Yinggehai Basin as the object, correlation analysis was first carried out to select and standardize the data features, and relevant effective parameters were extracted. Two kinds of neural networks, back-propagation network BP and back-propagation network GA-BP optimized by genetic algorithm, were used to establish artificial intelligence monitoring models of formation pressure based on 10 kinds of measuring and logging data, respectively. The application effect of the model was evaluated based on the results of monitoring the pressure while drilling. The results show that the monitoring accuracy of the BP neural network model is 91.25%, and that of the GA-BP neural network model is 92.89%. The latter has a better monitoring effect on formation pore pressure. In formation pressure monitoring in areas with a high degree of well control, the introduction of artificial intelligence technology has the advantages of simplicity, speed and high precision, and can provide a reference for other areas of pressure monitoring while drilling.

Analysis of Low-Frequency Sound Absorption Performance and Optimization of Structural Parameters for Acoustic Metamaterials for Spatial Double Helix Resonators

Low-frequency noise absorbers often require large structural dimensions, constraining their development in practical applications. In order to improve space utilization, an acoustic metamaterial with a spatial double helix, called a spatial double helix resonator (SDHR), is proposed in this paper. An analytical model of the spatial double-helix resonator is established and verified by numerical simulations and impedance tube experiments. By comparing the acoustic absorption coefficients of the spatial double-helix resonator, it is shown that the results of the analytical model, the numerical model, and the experiments are in good agreement, proving the accuracy of the theoretical model. The effects of different structural parameters on the peak sound absorption coefficient and resonance frequency are quantitatively revealed. The impedance variation law of the model is obtained, and the resistance and reactance distributions at the resonance frequency are analyzed. In the optimization model, the Back Propagation (BP) network is used to construct the mapping between the structural parameters and the resonance frequency and sound absorption coefficient, and this is used as the constraints of the equation, which is combined with Wild Horse Optimization (WHO) to establish the BP-WHO optimization model to minimize the volume of the spatial double helix resonator. The results show that, for a given noise frequency, the optimized structural parameters enhance the space utilization without affecting the performance of the space double helix resonator.

Application and Research of Mine Safety Management Strategy Based on the BP Network Model

Coal mine safety production is undoubtedly the cornerstone of the healthy, table, and sustainable development of China’s coal industry, and it also profoundly affects the effective implementation of the national energy strategy. In this context, exploring the application of network models in coal mine safety management strategies not only has significant practical significance, but also has profound development value. By integrating these factors, we can have a more comprehensive understanding of the causes of coal mine safety accidents and provide a scientific basis for formulating effective preventive measures. By collecting and analyzing various data in real-time during the coal mine production process, we can promptly detect abnormal situations, predict potential safety risks, and take corresponding measures for intervention and prevention. This dynamic monitoring and early warning mechanism can not only improve the safety of coal mine production, but also enhance the efficiency and efficiency of coal mine production. In the analysis process, we particularly focused on the advantages of Back Propagation (BP) neural network models in mine safety evaluation. BP neural network models can handle uncertainty and fuzzy information and have strong learning and adaptive capabilities. By constructing a safety evaluation model based on BP neural networks, we can more accurately evaluate the safety risks in the coal mine production process, providing strong support for formulating more scientific and reasonable safety management strategies.

Machine Learning Prediction of Fuel Cell Remaining Life Enhanced by Variational Mode Decomposition and Improved Whale Optimization Algorithm

In predicting the remaining lifespan of Proton Exchange Membrane Fuel Cells (PEMFC), it is crucial to accurately capture the multi-scale variations in cell performance. This study employs Variational Mode Decomposition (VMD) to decompose performance data into intrinsic modes, elucidating critical multi-scale dynamics vital for understanding the complex degradation processes in fuel cells. In addition to VMD, this research utilizes an Improved Whale Optimization Algorithm (IWOA) to optimize a Back Propagation (BP) Neural Network. The IWOA focuses on precise adjustments of weights and biases, enabling the BP network to effectively interpret complex nonlinear relationships within the dataset. This optimization enhances the predictive model’s reliability and stability. Extensive experimental evaluations demonstrate that the integration of VMD, and the learning capabilities of the IWOA-optimized BP network significantly improves the model’s accuracy and stability across multiple predictions, thereby increasing the reliability of lifespan predictions for PEMFCs. This methodology offers a robust framework for extending the operational life and efficiency of fuel cells.

Analysis of Weighted Factors Influencing Submarine Cable Laying Depth Using Random Forest Method

This study addresses the limitations of traditional methods used to analyze factors influencing submarine cable burial depth and emphasizes the underutilization of cable construction data. To overcome these limitations, a machine learning-based model is proposed. The model utilizes cable construction data from the East China Sea to predict the weight of factors influencing cable burial depth. Pearson correlation analysis and principal component analysis are initially employed to eliminate feature correlations. The random forest method is then used to determine the weights of factors, followed by the construction of an optimized backpropagation (BP) neural network using the ISOA-BP hybrid optimization algorithm. The model’s performance is compared with other machine learning algorithms, including support vector regression, decision tree, gradient decision tree, and the BP network before optimization. The results show that the random forest method effectively quantifies the impact of each factor, with water depth, cable length, deviation, geographic coordinates, and cable laying tension as the significant factors. The constructed ISOA-BP model achieves higher prediction accuracy than traditional algorithms, demonstrating its potential for quality control in cable laying construction and data-driven prediction of cable burial depth. This research provides valuable theoretical and practical implications in the field.

Novel static decoupling algorithm for the multi-axis wheel force sensor based on the Informer network

Ensuring the stability of heavy multi-axle vehicles necessitates the accurate calibration and decoupling of Multi-axis Wheel Force Sensors (MWFS). Traditional methods often neglect the temporal coupling present in MWFS output data, leading to reduced accuracy. This paper introduces an Improved Decoupling Algorithm (IDI) based on the Informer network, designed to temporally decouple MWFS and enhance precision. The Decoupling embedding layer (DE) performs linear decoupling of the MWFS, while the Token embedding layer (TE) and Informer encoder extract timing coupling features. The highway network and linear fully-connected layer then provide nonlinear decoupling compensation. Experimental results demonstrate that the IDI algorithm significantly outperforms traditional methods like Extreme Learning Machine (ELM) and Back Propagation Network (BPNN), achieving at least a 41.12% improvement in accuracy in the highly coupled Mz channel. In conclusion, the IDI algorithm not only achieves high-precision decoupling of MWFS but also presents a robust framework for modeling various sensor types.

Non-Intrusive Load Monitoring Based on Dimensionality Reduction and Adapted Spatial Clustering

Non-invasive load monitoring (NILM) deduces changes in energy consumption patterns and operational statuses of electrical equipment from power signals in the feed line. With the emergence of fine-grained power load distribution, the importance of utilizing this technology for implementing demand-side energy management in smart grid development has become increasingly prominent. To address the issue of low load identification accuracy stemming from complex and diverse load types, this paper introduces a NILM method based on uniform manifold approximation and projection (UMAP) reduction and enhanced density-based spatial clustering of applications with noise (DBSCAN). Firstly, this paper combines the characteristics of user load under transient and steady-state conditions and selects data with significant differences to construct a load-characteristic database. Additionally, UMAP is employed to reduce the dimensionality of high-dimensional load features and rebuild a load feature database. Subsequently, DBSCAN is utilized to categorize typical user loads, followed by a correlation analysis with the load-characteristic database to determine the types or classes of loads that involve switching actions. Finally, this paper simulates and analyzes the proposed method using the electricity consumption data of industrial users from the CER–Electricity–Data dataset. It identifies the electricity load data commonly utilized by users in a specific area of Zhejiang Province in China. The experimental results indicate that the accuracy of the proposed non-invasive load identification method reaches 95%. Compared to the wavelet transform, decision tree, and backpropagation network methods, the improvement is approximately 5%.

HybGBS: A hybrid neural network and grey wolf optimizer for intrusion detection in a cloud computing environment

SummaryThe cloud computing environment is subject to unprecedented cyber‐attacks as its infrastructure and protocols may contain vulnerabilities and bugs. Among these, Distributed Denial of Service (DDoS) is chosen by most cyber extortionists, creating unusual traffic that drains cloud resources, making them inaccessible to customers and end users. Hence, security solutions to combat this attack are in high demand. The existing DDoS detection techniques in literature have many drawbacks, such as overfitting, delay in detection, low detection accuracy for attacks that target multiple victims, and high False Positive Rate (FPR). In this proposed study, an Artificial Neural Network (ANN) based hybrid GBS (Grey Wolf Optimizer (GWO) + Back Propagation Network (BPN) + Self Organizing Map (SOM)) Intrusion Detection System (IDS) is proposed for intrusion detection in the cloud computing environment. The base classifier, BPN, was chosen for our research after evaluating the performance of a comprehensive set of neural network algorithms on the standard benchmark UNSW‐NS 15 dataset. BPN intrusion detection performance is further enhanced by combining it with SOM and GWO. Hybrid Feature Selection (FS) is made using a correlation‐based approach and Stratified 10‐fold cross‐validation (STCV) ranking based on Weight matrix value (W). These selected features are further fine‐tuned using metaheuristic GWO hyperparameter tuning based on a fitness function. The proposed IDS technique is validated using the standard benchmark UNSW‐NS 15 dataset, which consists of 1,75,341 and 82,332 attack cases in the training and testing datasets. This study's findings demonstrate that the proposed ANN‐based hybrid GBS IDS model outperforms other existing IDS models with a higher intrusion detection accuracy of 99.40%, fewer false alarms (0.00389), less error rate (0.001), and faster prediction time (0.29 ns).

Wind Speed Forecasting Based on Phase Space Reconstruction and a Novel Optimization Algorithm

The wind power generation capacity is increasing rapidly every year. There needs to be a corresponding development in the management of wind power. Accurate wind speed forecasting is essential for a wind power management system. However, it is not easy to forecast wind speed precisely since wind speed time series data are usually nonlinear and fluctuant. This paper proposes a novel combined wind speed forecasting model that based on PSR (phase space reconstruction), NNCT (no negative constraint theory) and a novel GPSOGA (a hybrid optimization algorithm that combines global elite opposition-based learning strategy, particle swarm optimization and the genetic algorithm) optimization algorithm. SSA (singular spectrum analysis) is firstly applied to decompose the original wind speed time series into IMFs (intrinsic mode functions). Then, PSR is employed to reconstruct the intrinsic mode functions into input and output vectors of the forecasting model. A combined forecasting model is proposed that contains a CBP (cascade back propagation network), RNN (recurrent neural network), GRU (gated recurrent unit), and CNNRNN (convolutional neural network combined with recurrent neural network). The NNCT strategy is used to combine the output of the four predictors, and a new optimization algorithm is proposed to find the optimal combination parameters. In order to validate the performance of the proposed algorithm, we compare the forecasting results of the proposed algorithm with different models on four datasets. The experimental results demonstrate that the forecasting performance of the proposed algorithm is better than other comparison models in terms of different indicators. The DM (Diebold–Mariano) test, Akaike’s information criterion and the Nash–Sutcliffe efficiency coefficient confirm that the proposed algorithm outperforms the comparison models.

Utilizing neural networks and genetic algorithms in AI-assisted CFD for optimizing PCM-based thermal energy storage units with extended surfaces

Phase Change Materials (PCMs) offer significant benefits for applications such as electronic cooling and Thermal Energy Storage (TES) due to their high energy storage capacity and stability. However, their limited thermal conductivity restricts widespread utilization. To address this limitation, the use of fins has become a common technique to enhance heat transfer. Optimizing fin lengths and locations is challenging, as they affect natural convection. This study aims to develop a model using a feed-forward back-propagation network, trained on simulation data, for designing a TES unit. A design matrix, incorporating factors such as fin lengths and heights, is obtained through response surface methodology. Single- and multi-objective Genetic Algorithms (GAs) are employed to seek optimal solutions, considering Complete Melting Time (CMT) and total fin length. The results demonstrate the excellent performance of the network model, with an error of less than 2.98%. The single-objective GA yields a minimum CMT. In a finless enclosure, the CMT is 338.5% higher compared to the single-objective configuration. The multi-objective GA, using Euclidean distance specified from the Pareto front to balance weight and material costs, results in a CMT that is 192% higher compared to the single-objective optimization but with a total fin length that is 286% shorter. While these improvements are significant, it is important to note that fins can increase material and manufacturing costs, as well as the overall unit weight. This artificial intelligence-computational fluid dynamics method not only predicts TES unit performance but also reduces computational costs in designing an optimal TES unit.

Addressing antecedents’ importance of open innovation between industry and universities: A neural network-based importance-performance analysis with a fuzzy approach

Determining the importance of major antecedents of open innovation between such distinct partners as industry and universities influences the decision-making regarding resources and effort allocation to their improvement, according to the strategic objectives of the firms. For this purpose, the present paper proposes an approach for conducting their importance-performance analysis based on fuzzy set theory and neural networks. Considering a hierarchical component model that integrates the components of the major antecedents, this study advances a research framework that first involves the operationalization of the collected data as fuzzy numbers. Then, the SHapley Additive exPlanation-based method estimates the derived importance of each component in the hierarchical component model using an optimal two-layers back-propagation network. Finally, a nine quadrants division of the importance-performance analysis developed on the basis of relevance and determinance measures of the analyzed antecedent components, delineates the prioritization of their potential improvements. A case study aims to demonstrate the developed research framework, illustrating its effectiveness and flexibility in decision-making related to the improvement of such antecedents.

An Early Warning Model for Turbine Intermediate-Stage Flux Failure Based on an Improved HEOA Algorithm Optimizing DMSE-GRU Model

As renewable energy sources such as wind and photovoltaics continue to enter the grid, their intermittency and instability leads to an increasing demand for peaking and frequency regulation. An efficient dynamic monitoring method is necessary to improve the safety level of intelligent operation and maintenance of power stations. To overcome the insufficient detection accuracy and poor adaptability of traditional methods, a novel fault early warning method with careful consideration of dynamic characteristics and model optimization is proposed. A combined loss function is proposed based on the dynamic time warping and the mean square error from the perspective of both shape similarity and time similarity. A prediction model of steam turbine intermediate-stage extraction temperature based on the gate recurrent unit is then proposed, and the change in prediction residuals is utilized as a fault warning criterion. In order to further improve the diagnostic accuracy, a human evolutionary optimization algorithm with lens opposition-based learning is proposed for model parameter adaptive optimization. Experiments on real-world normal and faulty operational data demonstrate that the proposed method can improve the detection accuracy by an average of 1.31% and 1.03% compared to the long short-term memory network, convolutional neural network, back propagation network, extreme learning machines, gradient boosting decision tree, and LightGBM models.

Convolutional Neural Network-Based Estimation of Nitrogen Content in Regenerating Rice Leaves

Regenerated rice, characterized by single planting and double harvesting, saves labor and costs, significantly contributing to global food security. Hyperspectral imaging technology, which integrates image and spectral data, provides comprehensive, non-destructive, and pollution-free vegetation canopy analysis, making it highly effective for crop nutrient diagnosis. In this study, we selected two varieties of regenerated rice for field trials. Hyperspectral images were captured during key growth stages (flush, grouting, and ripening) of both the first and regenerated seasons. Utilizing a two-dimensional convolutional neural network (2D-CNN) as a deep feature extractor and a fully connected layer for nitrogen content prediction, we developed a robust model suitable for estimating nitrogen content in regenerated rice. The experimental results demonstrate that our method achieves a mean squared error (MSE) of 0.0008, significantly outperforming the back-propagation (BP) network and multiple linear regression by reducing the MSE by 0.0151 and 0.0247, respectively. It also surpasses the one-dimensional convolutional neural network (1D-CNN) by 0.003. This approach ensures accurate nitrogen content prediction throughout the growth cycle of regenerated rice, aiding in yield and economic benefit enhancement.

Urban flood detection with the augmentation of gradient boosting and machine learning for prior warning sign over vulnerable zones

<p>Urban flooding has severely threatened the ecosystem and human life in recent years. The key to managing stormwater is to understand what causes it. The forceful effects of building shape on urban floods should be addressed, which results in a significant underestimation of flood danger. Algorithms for data-driven machine learning shed light on how the placement of buildings affects urban flooding. This study aimed to identify the elements of flooding risk and their effects on nearby communities using a concatenated modelling loop that included the XGBoost algorithm. This work suggests an enhanced extreme gradient boosting (XGBoost) approach based on a concatenated boosting particle swarm optimization (CBPSO) operator to acquire the meteorological refractive index of 100 m over the ocean. The prediction results of the enhanced XGBoost algorithm are compared with those of the backpropagation (BP) network and the original XGBoost method using the evaluation criteria Accuracy, Precision, Recall, F1-score, and IoU.Moreover, the networks resolve the featured misaligned issue during the decoder by inserting a component synchronization module into the up-sampling procedure. The model's intersections of unions (IoU) of 89.90% outperformed SOTA flood detection systems.</p>

Genetic algorithm (GA)–backpropagation (BP) network approach for hardness prediction of austempered ductile iron (ADI)

Hardness serves as a crucial indicator for assessing the success of quenching treatment in the steel and iron industry, impacting the processability and wear properties of materials. In the present study, a dataset comprising 125 hardness values of the QT500-7 sample subjected to various austempering heat treatment parameters was utilised to train a neural network model for predicting the hardness of austempered ductile iron (ADI). The established model based on a genetic algorithm and error backpropagation algorithm demonstrates high accuracy in predicting the hardness of ADI if given heat treatment parameters. The mean square error of the model was about 1.019, indicating the reliability and precision of the model in predicting the hardness of ADI based on the specified heat treatment parameters.

A DEEP LEARNING MODEL FOR EFFECTIVE CYCLONE INTENSITY ESTIMATION

Tropical cyclones are extremely dangerous weather phenomena that cause significant damage to human life, property, economy, agriculture, and development. Currently, various methods are being used to estimate cyclone intensity. One such method is the objective deviation angle variance technique, which estimates the intensity of tropical cyclones from satellite infrared imagery by performing statistical analysis of the brightness of those images. The limitation of this method is that it requires images with properly marked cyclone centers. Another method to estimate cyclone intensity involves feature engineering and machine learning, however, which is a manual process. To address the limitations associated with the above conventional methods, a new approach is being proposed that uses a deep learning mechanism to design a CYCLONE NETWORK (CY-Net) model. This model will estimate the cyclone intensity by using INSAT-3D infrared (IR) images. The CY-Net model is developed based on structural, intensification, and landfall features along with biasing parameters such as wind speed, sea level pressure, and sea surface temperature. The INSAT 3D data is given as input to the model for training, testing, and validation. It undergoes convolution along with the Re-lu activation function to generate feature maps, max pooling, sub-convolution, and fully connected layers. The stochastic gradient descent factor is measured to implement the backpropagation. The Stochastic gradient and backpropagation network are implemented to obtain the best filter coefficients. The trained, tested, and validated model is deployed in Python-flask for web application and then hosted using the web servers for web application. This approach uses advanced machine learning techniques to estimate cyclone intensity and has the potential to improve accuracy and reduce manual effort.

Talent Cultivation Quality of Software Engineering Majors Based on Deep Learning

Online learning, to cultivate talents it is inevitable to encounter some pictures or videos with poor visual quality. Deep-learning algorithms are both data-hungry and expensive to compute. These algorithms work better after being trained on a broad and extensive collection of samples. The current moment deep learning methods must urgently make use of human intellect to address the issue in a way that reduces the most expensive effort computationally. This paper analyzes the current situation of software engineering talent cultivation quality of software engineering to enhance the quality of the education is improved by Hierarchically Gated Recurrent Neural Network (HGRNN). The aim of the work is to foster the development of world-class software engineering talents. Initially, the input data’s are gathered from public dataset train 400 with 400 grey pictures. HGRNN is image de-noising module, as for the smart teaching platform to assist instructors in obtaining teaching photography with high quality and improve teaching quality. The proposed model is implemented in MATLAB/ Simulink platform and the accuracy is compared to various existing approaches such Back Propagation Network (BPN), Artificial Neural Network (ANN) and Decision Tree Algorithm (DTA) our proposed method obtains 98% of accuracy.

Enhancing Bioactive Compound Classification through the Synergy of Fourier-Transform Infrared Spectroscopy and Advanced Machine Learning Methods.

Bacterial infections and resistance to antibiotic drugs represent the highest challenges to public health. The search for new and promising compounds with anti-bacterial activity is a very urgent matter. To promote the development of platforms enabling the discovery of compounds with anti-bacterial activity, Fourier-Transform Mid-Infrared (FT-MIR) spectroscopy coupled with machine learning algorithms was used to predict the impact of compounds extracted from Cynara cardunculus against Escherichia coli. According to the plant tissues (seeds, dry and fresh leaves, and flowers) and the solvents used (ethanol, methanol, acetone, ethyl acetate, and water), compounds with different compositions concerning the phenol content and antioxidant and antimicrobial activities were obtained. A principal component analysis of the spectra allowed us to discriminate compounds that inhibited E. coli growth according to the conventional assay. The supervised classification models enabled the prediction of the compounds' impact on E. coli growth, showing the following values for accuracy: 94% for partial least squares-discriminant analysis; 89% for support vector machine; 72% for k-nearest neighbors; and 100% for a backpropagation network. According to the results, the integration of FT-MIR spectroscopy with machine learning presents a high potential to promote the discovery of new compounds with antibacterial activity, thereby streamlining the drug exploratory process.