Three-layer Back-propagation Neural Network Research Articles

Research on quantitative inversion characterization of high-definition electrical imaging logging in oil-based mud based on backpropagation neural network and multiple population genetic algorithm-Levenberg-Marquardt algorithm

Electrical imaging logging in oil-based mud (OBM) has been developed for some time and is gradually playing an important role in the description of deep carbonate and shale reservoirs. Quantitative characterization of reservoir rock parameters such as resistivity is one of the most innovative developments in this field. The development of this technology needs to address and resolve four core issues: a wide range of parameter variations, removal of mud-cake influence in low-resistivity formations, dielectric rollover in high-resistivity formations, and multifrequency dielectric dispersion effects. To address the aforementioned issues, the joint use of a backpropagation neural network (BPNN) and the multiple population genetic algorithm (MPGA)-Levenberg-Marquardt (LM) algorithm for high-resolution quantitative imaging is developed. First, the numerical simulation is used to calculate the well-logging response data under the influence of multiple parameters, thereby establishing a forward response database. Then, within the forward response database, the instrument response function is fitted using BPNN, to compress the data volume. Next, based on the fitted response function, an inversion method for three parameters, including reservoir rock resistivity, permittivity, and plate standoff, is established using the LM algorithm optimized with MPGA. The results indicate that the use of a three-layer BPNN enables rapid and accurate calculation of the electrical imaging logging response in OBM. The calculation of a single point only requires 0.1 ms with an accuracy of more than 99%. The MPGA-LM algorithm exhibits stronger stability and improved inversion accuracy, with a single point inversion time of only 2 ms, and contributes to the high-definition quantitative description of electrical imaging logging in OBM, which is important in characterizing formation structures, distinguishing formation fractures, etc.

Optimizing asphalt foaming using neural network

AbstractThis study uses a three-layer backpropagation neural network combined with particle swarm optimization to control the foamed bitumen in cold recycling technology. The foaming process of bitumen is non-linear and depends on dynamic temperature. By developing a neural network model, this study effectively captures the complex relationships between temperature, water content, air pressure, and the expansion ratio and half-life of foamed bitumen. The integration of particle swarm optimization enhances the accuracy and convergence of the neural network model by optimizing the initial weights. This optimization process improves the model's ability to predict and control the quality of foamed bitumen accurately. It serves as a valuable tool for the rapid development of high-quality cold asphalt design.

Water-exit dynamics of a ventilated underwater vehicle in wave environments with a combination of computational fluid dynamics and machine learning

A ventilated vehicle exiting water in a wave environment is a complex nonlinear process, and the mechanism by which the wave conditions influence this process remains poorly understood. This paper describes realistic simulations of a ventilated vehicle exiting a water body under various wave conditions. Comprehensive analysis is conducted for a range of distinct wave scenarios, and a machine learning-based method is developed for the rapid forecasting of vehicle-related parameters. A three-layer backpropagation neural network is constructed, and its prediction performance is verified. Subsequently, predictive and optimization procedures are employed to determine the optimal wave phase for the water exit of the vehicle. Different wave conditions are shown to significantly affect the evolution of the ventilated cavity as well as the kinematic and loading characteristics of the vehicle. The pitch angular velocity and angle at the moment when the head of the vehicle reaches the free surface exhibit a positive cosine trend under different wave conditions. No regularity of the pitch angular velocity at the moment when the tail reaches the free surface is evident. The neural network exhibits exceptional proficiency in predicting the motion parameters and load characteristics of the vehicle. The optimal point for the vehicle to exit the water is determined to be at a wave phase of 0.125π, while the most hazardous point occurs when the wave phase is 1.1875π.

Enhancing plasticity in optoelectronic artificial synapses: A pathway to efficient neuromorphic computing

The continuous growth in artificial intelligence and high-performance computing has necessitated the development of efficient optoelectronic artificial synapses crucial for neuromorphic computing (NC). Ga2O3 is an emerging wide-bandgap semiconductor with high deep ultraviolet absorption, tunable persistent photoconductivity, and excellent stability toward electric fields, making it a promising component for optoelectronic artificial synapses. Currently reported Ga2O3 optoelectronic artificial synapses often suffer from complex fabrication processes and potential room for improvement due to plasticity. To address the issue of low device plasticity and practical application scenarios, we present an amorphous Ga2O3 (α-GaOx) flexible optoelectronic artificial synapse. This synapse modulates light stimulus signals using electron/oxygen vacancies and optical stimulation and operates as a visual storage device for information processing. We investigate the improvement of the optoelectronic synapses' plasticity by controlling the number of oxygen vacancies via a plasma treatment method and demonstrate its effective application in a three-layer backpropagation neural network for handwritten digit classification. Under the same stimulus conditions, the synaptic weight of samples treated with Ar plasma exhibits a higher rate of change, with the current levels increasing by 2–3 orders of magnitude, achieving greater plasticity. The improved optoelectronic synapses achieved an accuracy of 93.34%/94%, demonstrating their potential as efficient computing solutions and insights for future applications in NC chips.

Research and assessment of high sensitivity intensity modulation vector curvature sensor based on cascaded bamboo-joint microcavity structure

This study presents the VC-BM, a novel vector bamboo joint cascaded microcavity curvature sensor, which utilizes light intensity correlation. The device is fabricated using arc fusion, connecting a tapered seven-core optical fiber with a multi-mode and hollow-core fiber. Meanwhile, mode-coupling theory optimizes its design for high-quality transmission spectra. The feasibility and sensitivity of vector curvature sensing via intensity modulation (VC-BM) have been demonstrated through theoretical simulations and experimental validation. Building upon this foundation, a novel envelope intensity differential modulation algorithm is proposed, which maintains curvature direction recognition and enhances sensitivity, reaching up to 243.57 ± 3.63 dB/m−1. Additionally, leveraging its advantages, the successful integration of a three-layer Backpropagation (BP) neural network has enabled predictive analysis of this curvature sensing device, thereby increasing its commercial value. Furthermore, the sensor's high stability and resistance to temperature interference make it an ideal solution for wearable micro-deformation monitoring.

INTERNET TRAFFIC ZONE IDENTIFICATION BY BACKPROPAGATION AND PROBABILISTIC NEURAL NETWORKS

The article proposes an approach based on the concept of Artificial Intelligence for the categorization of urban areas of Internet content by corporate customers. The applicability of different neural apparatus was analyzed as well as three-layer Backpropagation Neural Networks (BPN) and four-layer Probabilistic Neural Networks (PNN) as the most suitable for the purpose of the study were selected. The synthesis of BPN architectures for Internet traffic identification was carried out according to a different number of computing units in the hidden layers with hyperbolic tangent sigmoid, log-sigmoid and linear transfer functions. The variations of a set of specific criteria were examined as Accuracy, Mean-Squared Error, Mean Absolute Error, Correlation coefficients, etc. The selection of PNNs against the defined quality indicators was based on a stepwise increase of the spread indicator of the Kernel functions in a Radial-Basis (RB) structural layer by analogy similar to that applied to BPNs. In the research processes, high levels of neural recognition indicators were established in processing with the Incoming flows of Internet Packages in an Accuracy of over 90.00%. 

Roughness prediction of end milling surface for behavior mapping of digital twined machine tools

Background: The quality of machined parts is considered as a relevant factor to evaluate the production performance of machine tools. For mapping the production performance into a digital twin machine tool, a virtual metrology model for surface roughness prediction, which affects products' mechanical capacity and aesthetic performance, is proposed in this paper. Methods: The proposed model applies a three-layer backpropagation neural network by using real-time vibration, force, and current sensor data collected during the end milling machining process. A grid search plan is used to settle down the number of neurons in the middle layer of the backpropagation neural network. Results: The experimental results indicate that the model with multiple signals as input performs better than it with a single signal. In detail, when the model input is the combination of force, vibration, and current sensor data, the prediction accuracy reaches the optimum with the mean absolute percentage error of 1.01%. Conclusions: Compared with the state-of-the-art convolutional neural network method with automatic feature extraction ability and other commonly used traditional machine learning methods, the proposed data preprocessing procedure integrated with a three-layer backpropagation neural network has a minimum prediction error.

Simulation analysis of carbon peak path in China from a multi-scenario perspective: evidence from random forest and back propagation neural network models.

China faces tough challenges in the process of low-carbon transformation. To determine whether China can achieve its new 2030 carbon peaking and carbon intensity reduction commitments, accurate prediction of China's CO2 emissions is vital. In this paper, the random forest (RF) model was used to screen 26 carbon emission influencing factors, and seven indicators were selected as key variables for prediction. Subsequently, a three-layer back propagation (BP) neural network was constructed to forecast China's CO2 emissions and intensity from 2020 to 2040 under the 13th Five-Year Plan, 14th Five-Year Plan, energy optimization, technology breakthrough, and dual control scenarios. The results showed that energy structure factors have the most significant impact on China's CO2 emissions, followed by technology level, and economic development factors are no longer the main drivers. Under the 14th Five-Year Plan scenario, China can achieve its carbon peaking on time, reaching 10,434.082 Mt CO2 emissions in 2030. Although the new commitment to intensity reduction (over 65%) under this scenario cannot be achieved, the 14th Five-Year Plan can bring about 73.359 and 539.710 Mt of CO2 reduction in 2030 and 2040 respectively, compared to the 13th Five-Year Plan. Under the technology breakthrough and dual control scenarios, China will meet its new commitments ahead of schedule, with the dual control scenario being the optimal pathway for CO2 emissions to peak at 9860.08 Mt in 2025. It is necessary for Chinese policy makers to adjust their current strategic planning, such as accelerating the transformation of energy structure and increasing investment in R&D to achieve breakthroughs in green technologies.

State of Health Estimation of Lithium-Ion Batteries Using a Multi-Feature-Extraction Strategy and PSO-NARXNN

The lithium-ion battery state of health (SOH) estimation is critical for maintaining reliable and safe working conditions for electric vehicles (EVs). However, accurate and robust SOH estimation remains a significant challenge. This paper proposes a multi-feature extraction strategy and particle swarm optimization-nonlinear autoregressive with exogenous input neural network (PSO-NARXNN) for accurate and robust SOH estimation. First, eight health features (HFs) are extracted from partial voltage, capacity, differential temperature (DT), and incremental capacity (IC) curves. Then, qualitative and quantitative analyses are used to evaluate the selected HFs. Second, the PSO algorithm is adopted to optimize the hyperparameters of NARXNN, including input delays, feedback delays, and the number of hidden neurons. Third, to verify the effectiveness of the multi-feature extraction strategy, the SOH estimators based on a single feature and fusion feature are comprehensively compared. To verify the effectiveness of the proposed PSO-NARXNN, a simple three-layer backpropagation neural network (BPNN) and a conventional NARXNN are built for comparison based on the Oxford aging dataset. The experimental results demonstrate that the proposed method has higher accuracy and stronger robustness for SOH estimation, where the average mean absolute error (MAE) and root mean square error (RMSE) are 0.47% and 0.56%, respectively.

A prediction model of wall shear stress for ultra-high-pressure water-jet nozzle based on hybrid BP neural network

ABSTRACT Two hybrid back-propagation neural network (BPNN) models optimized by two heuristic search algorithms, namely genetic algorithm (GA-BP) and particle swarm optimization (PSO-BP), are proposed in this paper to predict radial maximum wall shear stress instead of traditional computational fluid dynamics (CFD) methods. The two proposed models are trained and validated using a database of 150 radial maximum wall-shear-stress values obtained via CFD simulations. The best fit model is identified from various BPNN models based on the Akaike information criterion (AIC) and the Bayesian information criterion (BIC), balancing the trade-off between goodness-of-fit and model complexity. The model performance is evaluated by MAE, RMSE, regression coefficient (R), and Nash-Sutcliffe efficiency (NSE). The best fit model is a three-layered BPNN model consisting of a 4:4:1 topology. In almost all evaluation indicators, the two hybrid BPNN methods outperform three existing algorithms, namely classical BPNN, random forest (RF), and gradient boosting decision tree (GBDT). Both PSO-BP and GA-BP can provide a more precise assessment of radial maximum wall shear stress, with maximum errors being 5.81% and 8.24% respectively. The proposed PSO-BP prediction model is promising and has great feasibility in predicting the radial maximum wall shear stress of UHP water-jet nozzle in engineering applications.

Predictive Study of Flow-Accelerated Corrosion Characteristic Parameters Based on the Neural Network

Corrosion of equipment by corrosive media is widespread in the processing of inferior crude oil. In hydroprocessing reactor effluent systems, corrosive media are very destructive to heat exchangers and air coolers during flow and cooling because of the high-temperature and -pressure environment. A fire and explosion in the air cooler or heat exchanger are highly likely when their tubes leak. Currently, there are no effective direct detection and prediction means to evaluate the corrosion risk in real time, creating significant hidden threats to the safe operation of the equipment. Therefore, this paper proposes a condition expansion method based on a Gaussian distribution. The distribution laws of characteristic corrosion parameters under various working conditions were studied, and the corrosion risk of the equipment was evaluated. A three-layer back-propagation neural network model is constructed to predict the characteristic corrosion parameters. After testing, the model is shown to have superior predictive accuracy and generalization performance. It can also meet the demand for real-time equipment corrosion prediction. The proposed method can serve an essential role in guiding engineers to take correct and timely prevention and control measures for different degrees of corrosion to reduce losses.

Intelligent Monitoring System Based on Noise-Assisted Multivariate Empirical Mode Decomposition Feature Extraction and Neural Networks.

Because of the nonlinearity and nonstationarity in the vibration signals of some rotating machinery, the analysis of these signals using conventional time- or frequency-domain methods has some drawbacks, and the results can be misleading. In this paper, a couple of features derived from multivariate empirical mode decomposition (MEMD) are introduced, which overcomes the shortcomings of the traditional features. A wind turbine gearbox and its bearings are investigated as rotating machinery. In this method, two types of feature structures are extracted from the decomposed signals resulting from the MEMD algorithm, called intrinsic mode function (IMF). The first type of feature vector element is the energy moment of effective IMFs. The other type of vector elements is amplitudes of a signal spectrum at the characteristic frequencies. A correlation factor is used to detect effective IMFs and eliminate the redundant IMFs. Since the basic MEMD algorithm is sensitive to noise, a noise-assisted extension of MEMD, NA-MEMD, is exploited to reduce the effect of noise on the output results. The capability of the proposed feature vector in health condition monitoring of the system is evaluated and compared with traditional features by using a discrimination factor. The proposed feature vector is utilized in the input layer of the classical three-layer backpropagation neural network. The results confirm that these features are appropriate for intelligent fault detection of complex rotating machinery and can diagnose the occurrence of early faults.

Study on control-oriented emission predictions of PPCI diesel engine with two-stage fuel injection

Model predictive control (MPC) is an effective technology used to improve the control accuracy of low temperature combustion engines. The main purpose of this study is to establish a control-oriented emission prediction model based on fuel injection parameters, which provides the basis for the development of MPC controller for PPCI engines. In this study, the combustion model and backpropagation (BP) neural network emission prediction model of a partially premixed compression ignition (PPCI) diesel engine were established, through which the real-time combustion and emissions can be realized by inputting two-stage fuel injection parameters. The Wiebe function linearization method was proposed for the combustion model to achieve high-precision reconstruction of combustion at different fuel injection parameters. The nitrogen oxide (NOx) and particulate matter (PM) emissions were predicted using the combustion characteristics calculated by the combustion model. The results show that the established combustion model can accurately reconstruct the heat release rate and cylinder pressure of PPCI combustion, and the maximum error between the calculated and test values of the combustion parameters is less than 9%. The three-layer BP neural network can achieve high-precision predictions of NOx and PM emissions, with a prediction accuracy greater than 95%. Under the 300th continuous transient seconds, the cumulative prediction error of NOx was 2%, and the cumulative prediction error of PM was 4.9%, indicating high accuracy. This provides the basis for developing MPC controllers for PPCI engines.

Individual Travel Knowledge Graph-Based Public Transport Commuter Identification: A Mixed Data Learning Approach

Commuters are the stable travel group for the public transportation (PT) service system. Accurately identifying the PT commuters is conducive to promoting PT service quality and development of urban sustainable transportation. This paper extracts individual PT travel chain information and constructs individual travel knowledge graphs of PT passengers based on the association matching algorithm and the theory of multilayer planning. A mixed dataset is formed by associating individual travel chains with travel survey data. Seven travel characteristic indicators regarding travel performance and spatiotemporal travel characteristics are extracted. The identification model of PT commuters is developed based on a three-layer backpropagation neural network (BPNN). The optimal model structure of neuron node number, transfer function, and learning rate are discussed quantitatively according to the minimization of model errors. The evaluation indexes of overall accuracy and kappa coefficient of the constructed model are 94.5% and 87.9% separately. The results indicate that the model identification accuracy is acceptable, and the proposed characteristic indicators and systematic modelling procedure are effective. Then, the model performance is compared with the other five machine learning models further. The results confirm that the proposed model has a better identification accuracy and viability, and the model performance will improve with the increase of the sample size.

Pattern Recognition of Growth Characteristics Based on UHF PD Signals of Electrical Tree

In insulation system of power equipment, electrical tree grown under the effect of partial discharges (PD) is one of the main degradation processes leading to failure of high voltage polymeric insulation. PD signals are measured and analyzed for condition monitoring of electrical insulation. In this paper, the combine detection system of Ultra-High Frequency (UHF) PD and electrical tree was used to test PE samples. Based on the energy amplitude variation, the average energy and the average energy distribution of PD signals to identify the growth process of electrical tree, twenty-eight PD characteristic parameters were used as input parameters, such as the energy percentage of 16-layer wavelet decomposition, the wavelet coefficient parameter and the time-frequency domain parameter, which entered the three-layer Back Propagation Neural Network (BPNN) of forty-five hidden neurons, and then the accuracy of pattern recognition during the growth process of electrical tree can reach 99.93% after 5000 steps of training cycle. The consistency of calculation results and experimental results presents that the calculation method can reveal the relationship between PD signals and the growth of electrical tree, which lays the foundation for the process analysis of electrical tree and insulation state monitoring.

Social Media Marketing Optimization Method Based on Deep Neural Network and Evolutionary Algorithm

Under the background of the vigorous development of China’s market economy, the marketing mix is constantly updated, which promotes the all-round development of various industries. Social media marketing has formed a relatively solid theoretical and practical foundation, especially with the continuous updating and iteration of Internet technology and the improvement of people’s requirements for experience, and we must find ways to optimize the methods of social media marketing. This study mainly introduces several optimization methods of social media marketing based on deep neural networks and advanced algorithms, and the experiments of gradient-based back-propagation algorithm and adaptive Adam’s optimization algorithm show that the proposed optimization algorithm can easily achieve the global optimal state based on the combination of back-propagation algorithm and Adam’s optimization algorithm. Accuracy of marketing is very important, so we introduce a scheme of how to accurately market, and the scheme is effective. Firstly, the FCE model is constructed by a three-layer back-propagation neural network, and then, the data input layer is designed to achieve the effect of the model.

Study on neural network algorithm for detecting respirable dust in photoacoustic cavity

The traditional photoacoustic cavity has the advantages of simple structure, low cost, and easy integration with optical cavity technology, so it has significant advantages in the measurement of the optical characteristics of respirable dust. In order to meet the demand of high-precision respirable dust measurements in practical applications, it is necessary to improve the measurement accuracy of respirable dust by traditional photoacoustic spectroscopy technology. Therefore, the structure size of the photoacoustic cavity was determined by theoretical and simulation analysis. A system for measuring respirable dust by photoacoustic spectroscopy was designed, which was applied to the atmospheric respirable dust detection simultaneously with the cavity ring-down spectroscopy system. The results showed that the correlation between the two systems was poor. Therefore, the three-layer back propagation neural network algorithm was used to correct the photoacoustic response values, and the measured value of the cavity ring-down spectroscopy system was used as the reference truth value. The calibration results showed that the output value of the neural network model was in good agreement with the reference true value: the slope was above 0.96. The results showed that the neural network algorithm could effectively improve the measurement accuracy of the photoacoustic spectroscopy system to respirable dust, improve the linearity, and reduce the detection error.

Highly sensitive resistance-type flexible pressure sensor for cuffless blood-pressure monitoring by using neural network techniques

Blood pressure is an important parameter of a heart-pumping function, vascular peripheral resistance, and blood volume in human body. The non-invasive blood pressure measurement method has attracted tremendous attention due to its advantages of simple operation and comfort. In this paper, graft-modified MWCNTs and water-based polyurethane are used as filler and matrix, which are combined with simple dipping process to produce a resistance-type flexible pressure sensor with crack structure. The sensor with crack structure exhibits a high gauge factor (∼1582.7), fast response time (∼58 ms) and good repeatability (>2000 cycles) under 0–5% strain. By attaching the sensor to the radial artery of human wrist, the pulse waveform is accurately captured. By adopting the time-domain analysis method, in addition to the fast Fourier transform and threshold method which is employed to position and extract the characteristic value of pulse signal, a three-layer back propagation neural network is constructed to regress blood pressure value. The correlation coefficients of systolic blood pressure, diastolic blood pressure and mean blood pressure were 0.950, 0.875 and 0.947, respectively, which could meet the A-level standards of British Hypertension Society and Association for the Advancement of Medical Instrumentation. This study provides a good real-time and portable technology for home blood pressure measurement.

Computational model of grid cells based on back‐propagation neural network

To simulate the firing pattern of biological Grid cells (GCs), an efficient computational model based on back-propagation neural network (BPNN), which allows for the generation of regular hexagonal grids, is described. For each GC, it is associated with the output of a learned BPNN. The firing characteristics of GCs can be controlled by the adjustment factors (AFs) flexibly. During the vehicle's space exploration, GCs can exhibit hexagonal firing patterns with different characteristics according to the settings of the AFs. Simulation results validate the proposed model's accuracy as they are in agreement with GCs' biological experimental electroencephalogram results. Meanwhile, the application of the three-layer BPNN can greatly reduce the computational complexity, which lay a foundation for its application in realistic bio-inspired autonomous navigation systems.

A Data-Driven Investigation on Surface Electromyography Based Clinical Assessment in Chronic Stroke.

Background: Surface electromyography (sEMG) based robot-assisted rehabilitation systems have been adopted for chronic stroke survivors to regain upper limb motor function. However, the evaluation of rehabilitation effects during robot-assisted intervention relies on traditional manual assessments. This study aimed to develop a novel sEMG data-driven model for automated assessment.Method: A data-driven model based on a three-layer backpropagation neural network (BPNN) was constructed to map sEMG data to two widely used clinical scales, i.e., the Fugl–Meyer Assessment (FMA) and the Modified Ashworth Scale (MAS). Twenty-nine stroke participants were recruited in a 20-session sEMG-driven robot-assisted upper limb rehabilitation, which consisted of hand reaching and withdrawing tasks. The sEMG signals from four muscles in the paretic upper limbs, i.e., biceps brachii (BIC), triceps brachii (TRI), flexor digitorum (FD), and extensor digitorum (ED), were recorded before and after the intervention. Meanwhile, the corresponding clinical scales of FMA and MAS were measured manually by a blinded assessor. The sEMG features including Mean Absolute Value (MAV), Zero Crossing (ZC), Slope Sign Change (SSC), Root Mean Square (RMS), and Wavelength (WL) were adopted as the inputs to the data-driven model. The mapped clinical scores from the data-driven model were compared with the manual scores by Pearson correlation.Results: The BPNN, with 15 nodes in the hidden layer and sEMG features, i.e., MAV, ZC, SSC, and RMS, as the inputs to the model, was established to achieve the best mapping performance with significant correlations (r > 0.9, P < 0.001), according to the FMA. Significant correlations were also obtained between the mapped and manual FMA subscores, i.e., FMA-wrist/hand and FMA-shoulder/elbow, before and after the intervention (r > 0.9, P < 0.001). Significant correlations (P < 0.001) between the mapped and manual scores of MASs were achieved, with the correlation coefficients r = 0.91 at the fingers, 0.88 at the wrist, and 0.91 at the elbow after the intervention.Conclusion: An sEMG data-driven BPNN model was successfully developed. It could evaluate upper limb motor functions in chronic stroke and have potential application in automated assessment in post-stroke rehabilitation, once validated with large sample sizes.Clinical Trial Registration: www.ClinicalTrials.gov, identifier: NCT02117089.