A sub-grid scale model for Burgers turbulence based on the artificial neuralnetwork method

The present study proposes a sub-grid scale model for the one-dimensional Burgers turbulence based on the neuralnetwork and deep learning method.The filtered data of the direct numerical simulation is used to establish thetraining data set,the validation data set,and the test data set.The artificial neural network(ANN)methodand Back Propagation method are employed to train parameters in the ANN.The developed ANN is applied toconstruct the sub-grid scale model for the large eddy simulation of the Burgers turbulence in the one-dimensionalspace.The proposed model well predicts the time correlation and the space correlation of the Burgers turbulence.

Artificial neural network-based one-equation model for simulation of laminar-turbulent transitional flow

A mapping function between the Reynolds-averaged Navier-Stokes mean flow variables and transition intermittency factor is constructed by fully connected artificial neural network(ANN),which replaces the governing equation of the intermittency factor in transition-predictive Spalart-Allmaras(SA)-γmodel.By taking SA-γmodel as the benchmark,the present ANN model is trained at two airfoils with various angles of attack,Mach numbers and Reynolds numbers,and tested with unseen airfoils in different flow states.The a posteriori tests manifest that the mean pressure coefficient,skin friction coefficient,size of laminar separation bubble,mean streamwise velocity,Reynolds shear stress and lift/drag/moment coefficient from the present two-way coupling ANN model almost coincide with those from the benchmark SA-γmodel.Furthermore,the ANN model proves to exhibit a higher calculation efficiency and better convergence quality than traditional SA-γmodel.

Adaptive fuze-warhead coordination method based on BP artificial neural network

The appropriate fuze-warhead coordination method is important to improve the damage efficiency of air defense missiles against aircraft targets. In this paper, an adaptive fuze-warhead coordination method based on the Back Propagation Artificial Neural Network(BP-ANN) is proposed, which uses the parameters of missile-target intersection to adaptively calculate the initiation delay. The damage probabilities at different radial locations along the same shot line of a given intersection situation are calculated, so as to determine the optimal detonation position. On this basis, the BP-ANN model is used to describe the complex and highly nonlinear relationship between different intersection parameters and the corresponding optimal detonating point position. In the actual terminal engagement process, the fuze initiation delay is quickly determined by the constructed BP-ANN model combined with the missiletarget intersection parameters. The method is validated in the case of the single-shot damage probability evaluation. Comparing with other fuze-warhead coordination methods, the proposed method can produce higher single-shot damage probability under various intersection conditions, while the fuzewarhead coordination effect is less influenced by the location of the aim point.

Application of the back-error propagation artificial neural network(BPANN) on genetic variants in the PPAR-γ and RXR-α gene and risk of metabolic syndrome in a Chinese Han population

This study was aimed to explore the associations between the combined effects of several polymorphisms in the PPAR-γ and RXR-α gene and environmental factors with the risk of metabolic syndrome by back-error propaga- tion artificial neural network （BPANN）. We established the model based on data gathered from metabolic syndrome patients （n = 1012） and normal controls （n = 1069） by BPANN. Mean impact value （MIV） for each input variable was calculated and the sequence of factors was sorted according to their absolute MIVs. Generalized multifactor dimensionality reduction （GMDR） confirmed a joint effect of PPAR-9＂ and RXR-a based on the results from BPANN. By BPANN analysis, the sequences according to the importance of metabolic syndrome risk fac- tors were in the order of body mass index （BMI）, serum adiponectin, rs4240711, gender, rs4842194, family history of type 2 diabetes, rs2920502, physical activity, alcohol drinking, rs3856806, family history of hypertension, rs1045570, rs6537944, age, rs17817276, family history of hyperlipidemia, smoking, rs1801282 and rs3132291. However, no polymorphism was statistically significant in multiple logistic regression analysis. After controlling for environmental factors, A1, A2, B1 and B2 （rs4240711, rs4842194, rs2920502 and rs3856806） models were the best models （cross-validation consistency 10/10, P = 0.0107） with the GMDR method. In conclusion, the interaction of the PPAR-γ and RXR-α gene could play a role in susceptibility to metabolic syndrome. A more realistic model is obtained by using BPANN to screen out determinants of diseases of multiple etiologies like metabolic syndrome.

Fractional Order Environmental and Economic Model Investigations Using Artificial Neural Network

The motive of these investigations is to provide the importance and significance of the fractional order(FO)derivatives in the nonlinear environmental and economic(NEE)model,i.e.,FO-NEE model.The dynamics of the NEE model achieves more precise by using the form of the FO derivative.The investigations through the non-integer and nonlinear mathematical form to define the FO-NEE model are also provided in this study.The composition of the FO-NEEmodel is classified into three classes,execution cost of control,system competence of industrial elements and a new diagnostics technical exclusion cost.The mathematical FO-NEE system is numerically studied by using the artificial neural networks(ANNs)along with the Levenberg-Marquardt backpropagation method(ANNs-LMBM).Three different cases using the FO derivative have been examined to present the numerical performances of the FO-NEE model.The data is selected to solve the mathematical FO-NEE system is executed as 70%for training and 15%for both testing and certification.The exactness of the proposed ANNs-LMBM is observed through the comparison of the obtained and the Adams-Bashforth-Moulton database results.To ratify the aptitude,validity,constancy,exactness,and competence of the ANNs-LMBM,the numerical replications using the state transitions,regression,correlation,error histograms and mean square error are also described.

Advances in memristor based artificial neuron fabrication-materials,models,and applications

Spiking neural network(SNN),widely known as the third-generation neural network,has been frequently investigated due to its excellent spatiotemporal information processing capability,high biological plausibility,and low energy consumption characteristics.Analogous to the working mechanism of human brain,the SNN system transmits information through the spiking action of neurons.Therefore,artificial neurons are critical building blocks for constructing SNN in hardware.Memristors are drawing growing attention due to low consumption,high speed,and nonlinearity characteristics,which are recently introduced to mimic the functions of biological neurons.Researchers have proposed multifarious memristive materials including organic materials,inorganic materials,or even two-dimensional materials.Taking advantage of the unique electrical behavior of these materials,several neuron models are successfully implemented,such as Hodgkin–Huxley model,leaky integrate-and-fire model and integrate-and-fire model.In this review,the recent reports of artificial neurons based on memristive devices are discussed.In addition,we highlight the models and applications through combining artificial neuronal devices with sensors or other electronic devices.Finally,the future challenges and outlooks of memristor-based artificial neurons are discussed,and the development of hardware implementation of brain-like intelligence system based on SNN is also prospected.

A robust behavior of Feed Forward Back propagation algorithm of Artificial Neural Networks in the application of vertical electrical sounding data inversion

The applications of intelligent techniques have increased exponentially in recent days to study most of the non-linear parameters. In particular, the behavior of earth resembles the non- linearity applications. An efficient tool is needed for the interpretation of geophysical parameters to study the subsurface of the earth. Artificial Neural Networks （ANN） perform certain tasks if the structure of the network is modified accordingly for the purpose it has been used. The three most robust networks were taken and comparatively analyzed for their performance to choose the appropriate network. The single- layer feed-forward neural network with the back propagation algorithm is chosen as one of the well- suited networks after comparing the results. Initially, certain synthetic data sets of all three-layer curves have been taken tk^r training the network, and the network is validated by the field datasets collected from Tuticorin Coastal Region （78°7＇30＂E and 8°48＇45＂N）, Tamil Nadu, India. The interpretation has been done successfully using the corresponding learning algorithm in the present study. With proper training of back propagation networks, it tends to give the resistivity and thickness of the subsurface layer model of the field resistivity data concerning the synthetic data trained earlier in the appropriate network. The network is trained with more Vertical Electrical Sounding （VES） data, and this trained network is demon- strated by the field data. Groundwater table depth also has been modeled.

Establishment of constitutive relationship model for 2519 aluminum alloy based on BP artificial neural network

An isothermal compressive experiment using Gleeble 1500 thermal simulator was studied to acquire flow stress at different deformation temperatures, strains and strain rates. The artificial neural networks with the error back propagation(BP) algorithm was used to establish constitutive model of 2519 aluminum alloy based on the experiment data. The model results show that the systematical error is small(δ=3.3%) when the value of objective function is 0.2, the number of nodes in the hidden layer is 5 and the learning rate is 0.1. Flow stresses of the material under various thermodynamic conditions are predicted by the neural network model, and the predicted results correspond with the experimental results. A knowledge-based constitutive relation model is developed.

The Prediction of Propagation Loss of FM Radio Station Using Artificial Neural Network

In order to calculate the propagation loss of electromagnetic waves produced by a transmitter, a variety of models based on empirical and deterministic formulas are used. In this study, one of the artificial neural networks models, Levenberg-Marquardt algorithm, which is quite effective for predicting the propagation is used and the results obtained by this algorithm are compared with the simulation results based on ITU-R 1546 and Epstein-Peterson models. In this paper, the propagation loss of FM radio station using artificial neural networks models is studied depending on the Levenberg-Marquardt algorithm. For training the artificial neural network, as the input data;range (r), effective antenna height (h) and terrain irregularity (△H) parameters are involved and measured values are treated as the output data. The good results obtained in the city area reveal that the artificial neural network is a very efficient method to compute models which integrate theoretical and experimental data. Meanwhile, the results show that an ANN model performs very well compared with theoretical and empiric propagation models with regard to prediction accuracy, complexity, and prediction time. By comparing the results, the RMSE for Neural Network Model using Levenberg-Marquardt is 9.57, and it is lower than that of classical propagation model using Epstein-Peterson for which RMSE is 10.26.

A BP Artificial Neural Network Model for Earthquake Magnitude Prediction in Himalayas, India

The aim of this study is to evaluate the performance of BP neural network techniques in predicting earthquakes occurring in the region of Himalayan belt (with the use of different types of input data). These parameters are extracted from Himalayan Earthquake catalogue comprised of all minor, major events and their aftershock sequences in the Himalayan basin for the past 128 years from 1887 to 2015. This data warehouse contains event data, event time with seconds, latitude, longitude, depth, standard deviation and magnitude. These field data are converted into eight mathematically computed parameters known as seismicity indicators. These seismicity indicators have been used to train the BP Neural Network for better decision making and predicting the magnitude of the pre-defined future time period. These mathematically computed indicators considered are the clustered based on every events above 2.5 magnitude, total number of events from past years to 2014, frequency-magnitude distribution b-values, Gutenberg-Richter inverse power law curve for the n events, the rate of square root of seismic energy released during the n events, energy released from the event, the mean square deviation about the regression line based on the Gutenberg-Richer inverse power law for the n events, coefficient of variation of mean time and average value of the magnitude for last n events. We propose a three-layer feed forward BP neural network model to identify factors, with the actual occurrence of the earthquake magnitude M and other seven mathematically computed parameters seismicity indicators as input and target vectors in Himalayan basin area. We infer through comparing curve as observed from seismometer in Himalayan Earthquake catalogue comprised of all events above magnitude 2.5 mg, their aftershock sequences in the Himalayan basin of year 2015 and BP neural network predicting earthquakes in 2015. The model yields good prediction result for the earthquakes of magnitude between 4.0 and 6.0.

Preparation of ZrB_2-SiC Powders via Carbothermal Reduction of Zircon and Prediction of Product Composition by Back-Propagation Artificial Neural Network

Phase pure ZrB2-SiC composite powders were prepared after 1 450℃/3 h via carbothermal reduction route,by using ZrSiO4,B2O3 and carbon as the raw materials.The influences of firing temperature as well as the type and amount of additive on the phase composition of final products were detailedly investigated.The results indicated that the onset formation temperature of ZrB2-SiC was reduced to 1 400℃by the present conditions,and oxide additive（including CoSO4·7H2O,Y2O3 and TiO2）was effective in enhancing the decomposition of raw ZrSiO4,therefore accelerating the synthesis of ZrB2-SiC.Moreover,microstructural observation showed that the as-prepared ZrB2 and SiC respectively had well-defined hexagonal columnar and fibrous morphology.Furthermore,the methodology of back-propagation artificial neural networks（BP-ANNs）was adopted to establish a model for predicting the reaction extent（e g,the content of ZrB2-SiC in final product）in terms of various processing conditions.The results predicted by the as-established BP-ANNs model matched well with that of testing experiment（with a mean square error in 10^（-3） degree）,verifying good effectiveness of the proposed strategy.

Prediction of column failure modes based on artificial neural network

To implement the performance-based seismic design of engineered structures,the failure modes of members must be classified.The classification method of column failure modes is analyzed using data from the Pacific Earthquake Engineering Research Center(PEER).The main factors affecting failure modes of columns include the hoop ratios,longitudinal reinforcement ratios,ratios of transverse reinforcement spacing to section depth,aspect ratios,axial compression ratios,and flexure-shear ratios.This study proposes a data-driven prediction model based on an artificial neural network(ANN)to identify the column failure modes.In this study,111 groups of data are used,out of which 89 are used as training data and 22 are used as test data,and the ANN prediction model of failure modes is developed.The results show that the proposed method based on ANN is superior to traditional methods in identifying the column failure modes.

Stand basal area modelling for Chinese fir plantations using an artificial neural network model

Artificial neural network models are a popular estimation tool for fitting nonlinear relationships because they require no assumptions about the form of the fitting function,non-Gaussian distributions,multicollinearity,outliers and noise in the data.The problems of backpropagation models using artificial neural networks include determination of the structure of the network and overlearning courses.According to data from 1981 to 2008 from 15 permanent sample plots on Dagangshan Mountain in Jiangxi Province,a back-propagation artificial neural network model(BPANN)and a support vector machine model(SVM)for basal area of Chinese fir(Cunninghamia lanceolata)plantations were constructed using four kinds of prediction factors,including stand age,site index,surviving stem numbers and quadratic mean diameters.Artificial intelligence methods,especially SVM,could be effective in describing stand basal area growth of Chinese fir under different growth conditions with higher simulation precision than traditional regression models.SVM and the Chapman–Richards nonlinear mixed-effects model had less systematic bias than the BPANN.

ARTIFICIAL NEURAL NETWORKS-MODELING, PROGRAMMING AND APPLICATION IN MATERIAL HOT WORKING

The developments of modern mathematics and computer science make artificial neural networks become most useful tools in wide range of fields. Modeling methods of artificial neural networks are described in this paper. The programming technique by using Matlab neural networks toolbox is discussed. The application in Material Hot Working of neural networks is also introduced.

Artificial neural network models predicting the leaf area index:a case study in pure even-aged Crimean pine forests from Turkey

Background: Leaf Area Index(LAI) is an important parameter used in monitoring and modeling of forest ecosystems. The aim of this study was to evaluate performance of the artificial neural network(ANN) models to predict the LAI by comparing the regression analysis models as the classical method in these pure and even-aged Crimean pine forest stands.Methods: One hundred eight temporary sample plots were collected from Crimean pine forest stands to estimate stand parameters. Each sample plot was imaged with hemispherical photographs to detect the LAI. The partial correlation analysis was used to assess the relationships between the stand LAI values and stand parameters, and the multivariate linear regression analysis was used to predict the LAI from stand parameters. Different artificial neural network models comprising different number of neuron and transfer functions were trained and used to predict the LAI of forest stands.Results: The correlation coefficients between LAI and stand parameters(stand number of trees, basal area, the quadratic mean diameter, stand density and stand age) were significant at the level of 0.01. The stand age, number of trees, site index, and basal area were independent parameters in the most successful regression model predicted LAI values using stand parameters(R_(adj)~2=0.5431). As corresponding method to predict the interactions between the stand LAI values and stand parameters, the neural network architecture based on the RBF 4-19-1 with Gaussian activation function in hidden layer and the identity activation function in output layer performed better in predicting LAI(SSE(12.1040), MSE(0.1223), RMSE(0.3497), AIC(0.1040), BIC(-77.7310) and R^2(0.6392)) compared to the other studied techniques.Conclusion: The ANN outperformed the multivariate regression techniques in predicting LAI from stand parameters. The ANN models, developed in this study, may aid in making forest management planning in study forest stands.

Applying Artificial Neural Networks to Modeling the Middle Atmosphere

An artificial neural network （ANN） is used to model the middle atmosphere using a large number of TIMED/SABER limb sounding temperature profiles. A three-layer feed-forward network is chosen based on the back-propagation （BP） algorithm. Latitude, longitude, and height are chosen as the input vectors of the network while temperature is the output vector. The temperature observations during the period from 13 January through 16 March 2007, which are in the same satellite yaw, are taken as samples to train an ANN. Results suggest that the network has high quality for modeling spatial variations of temperature. Quantitative comparisons between the ANN outputs and those from the popular empirical NRLMSISE-00 model illustrate their generally consistent features and some specific differences. The NRLMSISE-00 model＇s zonal mean temperatures are too high by ～6 K-10 K near the stratopause, and the amplitude and phase of the planetary wave number 1 activity are different in some respects from the ANN simulations above 45-50 km, suggesting improvement is needed in the NRLMSISE-00 model for more accurate simulation near and above the stratopause.

PREDICTION OF FLOW STRESS OF HIGH-SPEED STEEL DURING HOT DEFORMATION BY USING BP ARTIFICIAL NEURAL NETWORK

The hot deformation behavior of TI (18W-4Cr-1V) high-speed steel was investigated by means of continuous compression tests performed on Gleeble 1500 thermomechan- ical simulator in a wide range of tempemtures (950℃-1150℃) with strain rotes of 0.001s-1-10s-1 and true strains of 0-0. 7. The flow stress at the above hot defor- mation conditions is predicted by using BP artificial neural network. The architecture of network includes there are three input parameters:strain rate,temperature T and true strain , and just one output parameter, the flow stress ,2 hidden layers are adopted, the first hidden layer includes 9 neurons and second 10 negroes. It has been verified that BP artificial neural network with 3-9-10-1 architecture can predict flow stress of high-speed steel during hot deformation very well. Compared with the prediction method of flow stress by using Zaped-Holloman parumeter and hyperbolic sine stress function, the prediction method by using BP artificial neurul network has higher efficiency and accuracy.

Application of Artificial Neural Networks Model as Analytical Tool for Groundwater Salinity

The main source of water in Gaza Strip is the shallow coastal aquifer. It is extremely deteriorated in terms of salinity which influenced by many variables. Studying the relation between these variables and salinity is often a complex and nonlinear process, making it suitable to model by Artificial Neural Networks (ANN). Initially, it is assumed that the salinity (represented by chloride concentration, mg/l) may be affected by some variables as: recharge rate, abstraction, abstraction average rate, life time and aquifer thickness. Data were extracted from 56 municipal wells, covering the area of Gaza Strip. After a number of modeling trials, the best neural network was determined to be Multilayer Perceptron network (MLP) with four layers: an input layer of 6 neurons, first hidden layer with 10 neurons, second hidden layer with 7 neurons and the output layer with 1 neuron which gives the final chloride concentration. The ANN model generated very good results depending on the high correlation between the observed and simulated values of chloride concentration. The correlation coefficient (r) was 0.9848. The high value of (r) showed that the simulated chloride concentration values using the ANN model were in very good agreement with the observed chloride concentration which mean that ANN model is useful and applicable for groundwater salinity modeling. ANN model was successfully utilized as analytical tool to study influence of the input variables on chloride concentration. It proved that chloride concentration in groundwater is reduced by decreasing abstraction, abstraction average rate and life time. Furthermore, it is reduced by increasing recharge rate and aquifer thickness.

Artificial neural network modeling of water quality of the Yangtze River system:a case study in reaches crossing the city of Chongqing

An effective approach for describing complicated water quality processes is very important for river water quality management. We built two artificial neural network(ANN) models,a feed-forward back-propagation(BP) model and a radial basis function(RBF) model,to simulate the water quality of the Yangtze and Jialing Rivers in reaches crossing the city of Chongqing,P. R. China. Our models used the historical monitoring data of biological oxygen demand,dissolved oxygen,ammonia,oil and volatile phenolic compounds. Comparison with the one-dimensional traditional water quality model suggest that both BP and RBF models are superior; their higher accuracy and better goodness-of-fit indicate that the ANN calculation of water quality agrees better with measurement. It is demonstrated that ANN modeling can be a tool for estimating the water quality of the Yangtze River. Of the two ANN models,the RBF model calculates with a smaller mean error,but a larger root mean square error. More effort to identify out the causes of these differences would help optimize the structures of neural network water-quality models.