Multilayer Neural Network Model Research Articles

Mode-Mixed Effects Based Intralayer-Dependent Impulsive Synchronization for Multiple Mismatched Multilayer Neural Networks.

This article focuses on the intralayer-dependent impulsive synchronization of multiple mismatched multilayer neural networks (NNs) with mode-mixed effects. Initially, a novel multilayer NN model that removes the one-to-one interlayer coupling constraint and introduces nonidentical model parameters is first established to meet diverse modeling requirements in complex applications. To help the multilayer target NNs with mismatched connection coefficients and time delays achieve synchronization, the hybrid controller is designed using intralayer-dependent impulsive control and switched feedback control approaches. Furthermore, the mode-mixed effects caused by the intralayer coupling delays and switched intralayer topologies are incorporated into the novel model and analysis method to ensure that the subsystems operating within the current switching interval can effectively use the topology information of the previous switching intervals. Then, a novel analysis framework including super-Laplacian matrix, augmented matrix, and mode-mixed methods is developed to derive the synchronization results. Finally, the main results are verified via the numerical simulation with secure communication.

Cryptocurrency price fluctuation and time series analysis through candlestick pattern of bitcoin and ethereum using machine learning

PurposeCandlestick charts are a key tool for the technical analysis of cryptocurrency price fluctuations. It is essential to examine trends in the time series of a financial asset when completing an analysis. To accurately examine its potential future performance, it must also consider how it has changed and been active during the period. The researchers created cryptocurrency trading algorithms in this study based on the traditional candlestick pattern.Design/methodology/approachThe data includes information on Bitcoin prices from early 2012 until 2021. Only the engulfing Candlestick model was able to anticipate changes in the price movements of Bitcoin. The traditional Harami model does not work with Bitcoin trading platforms because it has yet to generate profitable business results. An inverted Harami is a successful cryptocurrency trading method.FindingsThe inverted Harami approach accounts for 6.98 profit factor (PrF) and 74–50% of profitable (Pr) transactions, which favors a particularly long position. Additionally, the study discovered that almost all analyzed candlestick patterns forecast longer trends greater than shorter trends.Research limitations/implicationsTo statistically study its future potential return, examining how it has changed and been active over the years is necessary. Such valuations are the basis for trading strategies that could help traders and investors in the cryptocurrency market. Without sacrificing clarity or ease of application, the proposed approach has increased performance by up to 32.5% of mean absolute error (MAE).Originality/valueThis study is novel in that it used multilayer autoregressive neural network (MARN) models with crypto-net (CNM) in machine learning to analyze a time series of financial cryptocurrencies. Here, the primary study deals with time trends extracted through a neural network model. Then, the developed model was tested using Bitcoin and Ethereum. Finally, CNM validity was tested through linear regression.

Multilayer neural network models for critical temperature of cuprate superconductors

A multi-layer neural network is used to extract the value of a superconductor’s Tc of cuprate from two models. The first model extracts Tc from the structure’s chemical composition whereas the second model extracts Tc from the structure’s chemical composition and the lattice parameters. The back-propagation algorithm is used to find the empirical equation of Tc. It can calculate error signals and redistribute backward propagation signals. This paper studies four systems of cuprate superconductors: YBaCuO, BiSrCaCuO, TlBaCaCuO, and HgBaCaCuO. In the first model, the Tc of four high-temperature oxide superconductors are calculated as a function of eight parameters and one output, which is Tc. Although the same output is produced in the second model, it is produced as a function of eleven parameters. The eight parameters are superconductor type number (Bi2212, Bi2223, Hg1201, Hg1212, Hg1223, Y123, Y124, Y247, Tl1223, Tl2212, Tl2223), first component composition, second component composition, third component composition, fourth component composition, atomic number of doping type, doping composition, and oxygen composition of the first model. The previous parameters with the three lattice parameters a, b and c are used in the second model. The trained deep learning models have shown a high degree of performance in matching the trained distributions. After analysing the results, we deduce electronegativity plays an important role in increasing Tc of cuprate superconductors. Using the obtained Tc prediction model, the scope is expanded to include the eleven unexplored multi-element materials. Candidates for superconductors with a higher Tc that can be synthesized are proposed. By comparison with other models of machine learning, the suggested models in this paper give the highest Tc for predicting new cuprate superconductors.

Integrated analysis of wireline logs analysis, seismic interpretation, and machine learning for reservoir characterisation: Insights from the late Eocene McKee Formation, onshore Taranaki Basin, New Zealand

This study aims to integrate seismic and well data for re-assessment and delineating the geological and petrophysical characteristics of the Late Eocene McKee Formation. Despite the McKee Formation being a hydrocarbon-producing field, there have been no previous integrated studies conducted to characterize its deposits in the Taranaki Basin. To execute these tasks, the process involves the interpretation of seismic data, wireline analysis, and 3D porosity volume prediction using multi-layered feed-forward neural network models. The seismic data was interpreted and found to contain low displacement faults due to intricate geometric complexities, the contour map that was generated shows lateral extension and thickness variation, and the region of direct hydrocarbon indicators was detected using the root-mean-square. Additionally, the seismic inversion provides a comprehensive understanding of the acoustic impedance variations from low to high value, which signifies the presence of mudstone clasts content. Furthermore, in the wireline log analysis, the reservoir was found to be a Silici-clastic and carbonate field owing to the presence of dolomite, calcite, and quartz. After applying cutoffs, petrophysical parameters were evaluated. The estimated values for effective porosity, reservoir thickness, shale volume, and water and hydrocarbon saturation were 15.04 % to 17.29 %, 85.44 m to 122.41 m, 8.99 to 14.87 % and 12.97–27.10 %, and 72.9 % to 87.03 %, respectively. The outcome of this extensive research aids in accurately characterizing this region for the future prospectively of the field. This integrated study can be applied and adapted to other basins in New Zealand and the southwestern Pacific. The proposed workflow is also suitable for analogous scenarios in various basins worldwide.

Improving pressure drop predictions for R134a evaporation in corrugated vertical tubes using a machine learning technique trained with the Levenberg–Marquardt method

AbstractThe present investigation utilized a machine learning structure to ascertain the pressure drop in vertically positioned, corrugated copper tubes during the evaporation process of R134a. The evaporator was a counter-flow heat exchanger, in which R134a flowed in the inner corrugated tube and hot water flowed in the smooth annulus. Different evaporation mass fluxes (195–406 kg m-2 s-1) and heat fluxes (10.16–66.61 kW m-2) were used with artificial neural networks at different corrugation depths. A multilayer perceptron artificial neural network model with 13 neurons in the hidden layer was proposed. Tan-Sig and Purelin transfer functions were used in the network model developed with the Levenberg–Marquardt training algorithm. The dataset, which consisted of 252 data points, related to the evaporation process, was divided into training (70%), validation (15%), and testing (15%) groups in an arbitrary manner. The artificial neural network model has been demonstrated to effectively forecast the pressure drop that occurs during evaporation. The mean squared error was computed for the ΔP values observed during the evaporation processes, yielding a value of 1.96E-03. The artificial neural network exhibited a high correlation coefficient value of 0.94479. The estimation fluctuations exhibited a range of ± 10%, whereas the experimental and anticipated ΔP data demonstrated a divergence of ± 10.3%.

Estimation of monthly sunshine duration using satellite derived cloud data

AbstractSunshine duration (SD) is one of the critical meteorological parameters used in different fields of application such as climate, renewable energy and agriculture. In this respect, determination and/or estimation of the temporal and spatial variability of SD is critical. Meteorological satellite data/products can be used for estimating SD and in constructing their maps due to their frequent observation of large areas at once. In this study, a multilayer perceptron type artificial neural network model was built to estimate the monthly mean SD for Türkiye using the EUMETSAT CM SAF (Satellite Application Facility on Climate Monitoring) CFC (Cloud Fractional Coverage) and CTY (Cloud Type) data, GMTED2010 (Global Multi-resolution Terrain Elevation Data) data, month number and daylength. The datasets of 45 stations, spanning nine years (2005–2013), were used for training the model and 12 stations for testing and validating the simulated values. We have compared the results of our model with the ground-measured values for the whole period under consideration and the root mean square error (RMSE), mean absolute error (MAE), mean bias error (MBE) and the coefficient of determination (R2) were found as 0.7803 h, 0.6206 h, 0.1751 h and 0.9387, respectively. It has been shown that using the new generation cloud products such as CFC and CTY, elevation data such as GMTED2010 and daylength, it is possible to predict the SD for regions under the coverage of the satellite, in case no measurement is possible or may be unreliable, without needing any measured meteorological data.

Emerging Vanadium-Doped Cobalt Chloride Carbonate Hydroxide for Flexible Electrochromic Micro-Supercapacitor: Charged-State Prediction from RGB Input by ANN Model.

Multifunctional devices integrated with electrochromic and supercapacitance properties are fascinating because of their extensive usage in modern electronic applications. In this work, vanadium-doped cobalt chloride carbonate hydroxide hydrate nanostructures (V-C3H NSs) are successfully synthesized and show unique electrochromic and supercapacitor properties. The V-C3H NSs material exhibits a high specific capacitance of 1219.9Fg-1 at 1mVs-1 with a capacitance retention of 100% over 30000 CV cycles. The electrochromic performance of the V-C3H NSs material is confirmed through in situ spectroelectrochemical measurements, where the switching time, coloration efficiency (CE), and optical modulation (∆T) are found to be 15.7 and 18.8s, 65.85cm2C-1 and 69%, respectively. A coupled multilayer artificial neural network (ANN) model is framed to predict potential and current from red (R), green (G), and blue (B) color values. The optimized V-C3H NSs are used as the active materials in the fabrication of flexible/wearable electrochromic micro-supercapacitor devices (FEMSDs) through a cost-effective mask-assisted vacuum filtration method. The fabricated FEMSD exhibits an areal capacitance of 47.15mFcm-2 at 1mVs-1 and offers a maximum areal energy and power density of 104.78Whcm-2 and 0.04mWcm-2, respectively. This material's interesting energy storage and electrochromic properties are promising in multifunctional electrochromic energy storage applications.

Spatial Air Quality Assessment in Peninsular Malaysia: Insights from Environmetric Analyses and Artificial Neural Networks

The research investigated the spatial distribution of air quality in Peninsular Malaysia to offer crucial insights for mitigating air quality degradation. Utilizing historical data obtained from the Malaysian Department of Environment, eleven years' worth (2011-2021) of daily readings of PM10, O3 , SO2 , NO2 , and CO from thirty-four monitoring stations were subjected to environmetric techniques and Artificial Neural Network analysis. HACA successfully classified seven stations into the High Pollution Cluster (HPC), seven stations into the Moderate Pollution Cluster (MPC), and twenty stations into the Low Pollution Cluster (LPC). The discriminant analysis demonstrated a correct assignment rate of 91.87%, suggesting that all five parameters were able to discriminate with a significance level of p<0.0001. Principal Component Analysis (PCA) revealed two Variance Factors (VFs) across all clusters, with cumulative variances of 69.43% (HPC), 82.32% (MPC), and 62.16% (LPC) respectively. Furthermore, employing a Multi-Layer Perceptron Feed-Forward Artificial Neural Network (MLP-FF-ANN) model to predict API readings yielded a significant and strong correlation, as indicated by R2 =0.7774 and RMSE=9.9048. These findings hold significant potential in informing the adoption of effective preventative and management strategies.

Effective Permittivity of Compacted Granular Materials: Effects of Interfacial Polarization and Pore-filling Fluids

Interfacial polarization dominates the permittivity spectra of heterogeneous granular materials for the intermediate frequency range (i.e., from kHz to MHz). In this study, we examine the corresponding dielectric responses of compacted glass sphere packings saturated with pore-filling fluids under various compressive stresses. The effective permittivity spectra are observed to exhibit consistently a plateau-to-plateau drop, described by low-frequency permittivity, characteristic frequency, and high-frequency permittivity. The permittivity spectra under different compressive levels are found to be influenced by the packing structure, compressive stress, and electrical property contrasts between solid and fluid (specifically permittivity and conductivity). For considered measurement conditions, the variation of packing structure and its associated porosity is found to be more significant than the stress evolution in controlling the interfacial polarization, thus the permittivity spectra, as supported by analytical and numerical results for unit cells. Furthermore, to gain a general rule for dielectric responses for saturated granular materials, we train multi-layer artificial neural network (ANN) models based on a series of simulations for unit cells with various structures, stresses, and electrical and dielectric properties. The predictions with two-layer ANN agree well with experimental measurements, presenting errors smaller than 5% for both low-frequency and high-frequency permittivity. This study offers an effective predicting approach for the dielectric behaviour of heterogeneous and multiphase materials.

Optimization of 2024-T3 Aluminum Alloy Friction Stir Welding Using Random Forest, XGBoost, and MLP Machine Learning Techniques.

This study optimized friction stir welding (FSW) parameters for 1.6 mm thick 2024T3 aluminum alloy sheets. A 3 × 3 factorial design was employed to explore tool rotation speeds (1100 to 1300 rpm) and welding speeds (140 to 180 mm/min). Static tensile tests revealed the joints' maximum strength at 87% relative to the base material. Hyperparameter optimization was conducted for machine learning (ML) models, including random forest and XGBoost, and multilayer perceptron artificial neural network (MLP-ANN) models, using grid search. Welding parameter optimization and extrapolation were then carried out, with final strength predictions analyzed using response surface methodology (RSM). The ML models achieved over 98% accuracy in parameter regression, demonstrating significant effectiveness in FSW process enhancement. Experimentally validated, optimized parameters resulted in an FSW joint efficiency of 93% relative to the base material. This outcome highlights the critical role of advanced analytical techniques in improving welding quality and efficiency.

Application of Machine Learning and Optimization of Oil Recovery and CO2 Sequestration in the Tight Oil Reservoir

Summary In recent years, shale and tight reservoirs have become an essential source of hydrocarbon production since advanced multistage and horizontal drilling techniques were developed. Tight oil reservoirs contain huge oil reserves but suffer from low recovery factors. For tight oil reservoirs, CO2-water alternating gas (CO2-WAG) is one of the preferred tertiary methods to enhance the overall cumulative oil production while also sequestering significant amounts of injected CO2. However, the evaluation of CO2-WAG is strongly dependent on the injection parameters, which renders numerical simulations computationally expensive. In this study, a novel approach has been developed that utilized machine learning (ML)-assisted computational workflow in optimizing a CO2-WAG project for a low-permeability oil reservoir considering both hydrocarbon recovery and CO2 storage efficacies. To make the predictive model more robust, two distinct proxy models—multilayered neural network (MLNN) models coupled with particle swarm optimization (PSO) and genetic algorithms (GAs)—were trained and optimized to forecast the cumulative oil production and CO2 storage. Later, the optimized results from the two algorithms were compared. The optimized workflow was used to maximize the predefined objective function. For this purpose, a field-scaled numerical simulation model of the Changqing Huang 3 tight oil reservoir was constructed. By December 2060, the base case predicts a cumulative oil production of 0.368 million barrels (MMbbl) of oil, while the MLNN-PSO and MLNN-GA forecast 0.389 MMbbl and 0.385 MMbbl, respectively. As compared with the base case (USD 150.5 million), MLNN-PSO and MLNN-GA predicted a further increase in the oil recovery factor by USD 159.2 million and USD 157.6 million, respectively. In addition, the base case predicts a CO2 storage amount of 1.09×105 tons, whereas the estimates from MLNN-PSO and MLNN-GA are 1.26×105 tons and 1.21×105 tons, respectively. Compared with the base case, CO2 storage for the MLNN-PSO and MLNN-GA increased by 15.5% and 11%, respectively. In terms of the performance analysis of the two algorithms, both showed remarkable performance. PSO-developed proxies were 16 times faster and GA proxies were 10 times faster as compared with the reservoir simulation in finding the optimal solution. The developed optimization workflow is extremely efficient and computationally robust. The experiences and lessons will provide valuable insights into the decision-making process and in optimizing the Changqing Huang 3 low-permeability oil reservoir.

Exploring the effectiveness of a multilayer neural network model for gold price prediction

Exploring the effectiveness of a multilayer neural network model for gold price prediction

Deep Graph Learning-Based Surrogate Model for Inverse Modeling of Fractured Reservoirs

Inverse modeling can estimate uncertain parameters in subsurface reservoirs and provide reliable numerical models for reservoir development and management. The traditional simulation-based inversion method usually requires numerous numerical simulations, which is time-consuming. Recently, deep learning-based surrogate models have been widely studied as an alternative to numerical simulation, which can significantly improve the solving efficiency of inversion. However, for reservoirs with complex fracture distribution, constructing the surrogate model of numerical simulation presents a significant challenge. In this work, we present a deep graph learning-based surrogate model for inverse modeling of fractured reservoirs. Specifically, the proposed surrogate model integrates the graph attention mechanisms to extract features of fracture network in reservoirs. The graph learning can retain the discrete characteristics and structural information of the fracture network. The extracted features are subsequently integrated with a multi-layer recurrent neural network model to predict the production dynamics of wells. A surrogate-based inverse modeling workflow is then developed by combining the surrogate model with the differential evolutionary algorithm. Numerical studies performed on a synthetic naturally fractured reservoir model with multi-scale fractures illustrate the performance of the proposed methods. The results demonstrate that the proposed surrogate model exhibits promising generalization performance of production prediction. Compared with tens of thousands of numerical simulations required by the simulation-based inverse modeling method, the proposed surrogate-based method only requires 1000 to 1500 numerical simulations, and the solution efficiency can be improved by ten times.

Hybrid MLP-CNN-based ground sink susceptibility prediction in urban area using underground pipe map

In urban areas, ground sinks are generally attributed to underground pipe damages resulting in leakage. Hence, it is essential to forecast ground sinks in advance because they might cause detrimental effects on infrastructure, including the roads, bridges, and railroads. Prior studies have focused on geological and topographical factors, with an inconsiderable amount of attention paid to the study of underground pipes. This study proposes a hybrid multilayer perceptron-convolutional neural network (MLP-CNN) model to predict ground sink susceptibility in urban areas using an underground pipe map. A dataset was constructed by extracting table and image data from the underground pipe map, and it was used to train the model. The test results indicated that the model could accurately predict ground sink susceptibility with an accuracy of 93.87 % and f1-score of 0.9478. Furthermore, we generated a ground sink susceptibility map (GSSM) using a trained model. The findings of this study confirmed that the hybrid MLP-CNN model is capable of reliably predicting ground sink susceptibility.

Advancing nanofluid technology: Experimental thermal conductivity measurements and accurate predictive models for yttrium oxide-water nanofluid using neural networks and mathematical correlations

This work involved the experimental measurement of thermal conductivity (TC) values for a novel nanofluid. This nanofluid was created by incorporating yttrium oxide nanoparticles into pure water, which served as the base fluid. The TC values of the nanofluid consisting of yttrium oxide and water were empirically determined at five distinct concentrations. These measurements were conducted within the temperature range of 10–65 degrees Celsius. The data obtained from the experiment demonstrated a 1.47% enhancement in the TC of the nanofluid consisting of yttrium oxide and water, in comparison to the base fluid. Experimental results were compared with correlations commonly used in the literature. A multilayer neural network model and a novel mathematical correlation were built utilizing a dataset of 60 experimental observations to ascertain the relationship between temperature, concentration, and the TC of yttrium oxide-water nanofluid. Nine data sets were used for validation of the network model, and in addition, the model was validated with various performance parameters. The values derived from the network model and mathematical correlation were juxtaposed with both the empirical findings and the outcomes documented in existing literature. The R value for the network model was calculated as 0.99980 and the MSE value was 5.89E-07. The average deviation rate for the network model was determined to be −0.0007%, whereas the mathematical correlation yielded a value of 0.049%. The study’s results demonstrated the development of a neural network model and a novel mathematical connection for accurately calculating the TC of yttrium oxide-water nanofluid.

Using Deep-Learning for 5G End-to-End Delay Estimation Based on Gaussian Mixture Models

Deep learning is used in various applications due to its advantages over traditional Machine Learning (ML) approaches in tasks encompassing complex pattern learning, automatic feature extraction, scalability, adaptability, and performance in general. This paper proposes an end-to-end (E2E) delay estimation method for 5G networks through deep learning (DL) techniques based on Gaussian Mixture Models (GMM). In the first step, the components of a GMM are estimated through the Expectation-Maximization (EM) algorithm and are subsequently used as labeled data in a supervised deep learning stage. A multi-layer neural network model is trained using the labeled data and assuming different numbers of E2E delay observations for each training sample. The accuracy and computation time of the proposed deep learning estimator based on the Gaussian Mixture Model (DLEGMM) are evaluated for different 5G network scenarios. The simulation results show that the DLEGMM outperforms the GMM method based on the EM algorithm, in terms of the accuracy of the E2E delay estimates, although requiring a higher computation time. The estimation method is characterized for different 5G scenarios, and when compared to GMM, DLEGMM reduces the mean squared error (MSE) obtained with GMM between 1.7 to 2.6 times.

Gesture Classification in Electromyography Signals for Real-Time Prosthetic Hand Control Using a Convolutional Neural Network-Enhanced Channel Attention Model.

Accurate and real-time gesture recognition is required for the autonomous operation of prosthetic hand devices. This study employs a convolutional neural network-enhanced channel attention (CNN-ECA) model to provide a unique approach for surface electromyography (sEMG) gesture recognition. The introduction of the ECA module improves the model's capacity to extract features and focus on critical information in the sEMG data, thus simultaneously equipping the sEMG-controlled prosthetic hand systems with the characteristics of accurate gesture detection and real-time control. Furthermore, we suggest a preprocessing strategy for extracting envelope signals that incorporates Butterworth low-pass filtering and the fast Hilbert transform (FHT), which can successfully reduce noise interference and capture essential physiological information. Finally, the majority voting window technique is adopted to enhance the prediction results, further improving the accuracy and stability of the model. Overall, our multi-layered convolutional neural network model, in conjunction with envelope signal extraction and attention mechanisms, offers a promising and innovative approach for real-time control systems in prosthetic hands, allowing for precise fine motor actions.

Combining hypothesis-driven ECG indices with machine learning improves ventricular arrhythmic risk prediction in a low-risk population

Abstract Background Life-threatening ventricular arrhythmias (LTVA) are a leading cause of mortality, but early identification of high-risk individuals remains a major challenge. An ECG index, TMV (T-wave morphology variations), predicts LTVA in 51,794 individuals without known cardiovascular disease from the UK Biobank (UKB) within a 10-year follow up (cohort 1), with an area under the ROC curve (AUC) of 0.558 and a hazard ratio (HR) of 1.57. Machine learning on the ECG has shown a high accuracy in detecting patients with cardiovascular disease, but its LTVA predictive value in a low-risk population and comparisons with hypothesis-driven risk markers, like TMV, is unknown. Purpose We tested the LTVA predictive value of a score combining a multi-layer convolutional neural network (CNN) model and TMV in individuals without known cardiovascular disease. Methods Using UKB we split the individuals from cohort 1 into training (90%) and internal test (10%) sets (Figure 1). In the training set, we applied 10-fold cross validation to train a multi-layer CNN with an attention layer, where the input was a 15-second ECG at rest (lead I), and the output was occurrence (or not) of LTVA within a 10-year follow-up. We used 10-second ECG data (lead I) from an independent cohort of 32,209 individuals without cardiovascular disease from UKB (cohort 2) as an external test set (median follow-up was 3.4 years), where we also calculated TMV. We next combined them into a score (weighted by their relative contribution to LTVA in a logistic regression). The AUC, and Cox regression HRs were calculated to estimate the performance of the CNN, TMV and the combined score. Results In cohort 1, 217 subjects had a LTVA during the follow-up period. In the internal test set, the AUC of the CNN was 0.707. In the external test set (60 LTVA events during the follow-up period), the CNN’s prediction and TMV led to AUCs of 0.610 and 0.604, respectively. Interestingly, the CNN’s prediction and TMV were not correlated (ρ = 0.005), and the combined score led to an AUC of 0.627. We set a threshold at the score value that maximised sensitivity and specificity, and survival analyses showed a HR of 3.007 (P < 0.0001) for individuals in the score+ (score > threshold) versus those in the score- (score < threshold) group, after adjusting for age and gender (Figure 2). The continuous score also predicted LTVA independently from age and gender. Conclusions A CNN-based model predicts LTVA in a low-risk population independently from TMV, a strong ECG risk predictor derived from knowledge on the underlying electrophysiology. When TMV and the CNN are combined into a score, the LTVA risk stratification improves, independently from age and gender. Our findings support the combination of hypothesis-based risk markers and CNN approaches to optimise LTVA prediction in a low-risk population.Study designSurvival curves

A preferential interpretation of MultiLayer Perceptrons in a conditional logic with typicality

In this paper we investigate the relationships between a multipreferential semantics for defeasible reasoning in knowledge representation and a multilayer neural network model. Weighted knowledge bases for a simple description logic with typicality are considered under a (many-valued) “concept-wise” multipreference semantics. The semantics is used to provide a preferential interpretation of MultiLayer Perceptrons (MLPs). A model checking and an entailment based approach are exploited in the verification of conditional properties of MLPs.

Lump wave solutions, lump-stripe soliton inelastic collision phenomena and rogue-type wave solutions for a generalized breaking soliton system in (3+1)-dimensions

Based on five types of structures of the functional multi-layer neural network model, some explicit exact solutions for a (3+1)-dimensional generalized breaking soliton system including lump waves, lump-stripe solitons and rogue-type waves are explored by using bilinear neural network method without the datasets training. The 3-dimensional plots, 2-dimensional curves and density plots of these results obtained are provided to display their dynamic characteristics. In particular, the complete processes of lump wave and stripe soliton fusion phenomena are presented. We hope the formulations established in this paper could be applied to study the exact traveling wave solutions of other integrable nonlinear evolution equations.