scholarly journals An artificial neural network technique for downscaling GCM outputs to RCM spatial scale

2011 ◽  
Vol 18 (6) ◽  
pp. 1013-1028 ◽  
Author(s):  
R. Chadwick ◽  
E. Coppola ◽  
F. Giorgi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Regional Climate ◽  
Correction Method ◽  
Lapse Rate ◽  
Climate Modelling ◽  
The Neural Network ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
The Relationship

Abstract. An Artificial Neural Network (ANN) approach is used to downscale ECHAM5 GCM temperature (T) and rainfall (R) fields to RegCM3 regional model scale over Europe. The main inputs to the neural network were the ECHAM5 fields and topography, and RegCM3 topography. An ANN trained for the period 1960–1980 was able to recreate the RegCM3 1981–2000 mean T and R fields with reasonable accuracy. The ANN showed an improvement over a simple lapse-rate correction method for T, although the ANN R field did not capture all the fine-scale detail of the RCM field. An ANN trained over a smaller area of Southern Europe was able to capture this detail with more precision. The ANN was unable to accurately recreate the RCM climate change (CC) signal between 1981–2000 and 2081–2100, and it is suggested that this is because the relationship between the GCM fields, RCM fields and topography is not constant with time and changing climate. An ANN trained with three ten-year "time-slices" was able to better reproduce the RCM CC signal, particularly for the full European domain. This approach shows encouraging results but will need further refinement before becoming a viable supplement to dynamical regional climate modelling of temperature and rainfall.

A Model on the Correlation between Composition and Mechanical Properties of Mg-Al-Zn Alloys by Using Artificial Neural Network

2005 ◽  
Vol 488-489 ◽  
pp. 793-796 ◽  
Author(s):  
Hai Ding Liu ◽  
Ai Tao Tang ◽  
Fu Sheng Pan ◽  
Ru Lin Zuo ◽  
Ling Yun Wang
Keyword(s):  
Neural Network ◽  
Mechanical Properties ◽  
Artificial Neural Network ◽  
Magnesium Alloys ◽  
Tensile Yield Strength ◽  
Data Set ◽  
The Neural Network ◽  
Artificial Neural ◽  
Prediction Of Mechanical Properties ◽  
Artificial Neural Network Ann

A model was developed for the analysis and prediction of correlation between composition and mechanical properties of Mg-Al-Zn (AZ) magnesium alloys by applying artificial neural network (ANN). The input parameters of the neural network (NN) are alloy composition. The outputs of the NN model are important mechanical properties, including ultimate tensile strength, tensile yield strength and elongation. The model is based on multilayer feedforward neural network. The NN was trained with comprehensive data set collected from domestic and foreign literature. A very good performance of the neural network was achieved. The model can be used for the simulation and prediction of mechanical properties of AZ system magnesium alloys as functions of composition.

scholarly journals Solving multiple linear regression problem using artificial neural network

2022 ◽  
Vol 12 (1) ◽  
pp. 770
Author(s):  
Mohammad S. Khrisat ◽  
Ziad A. Alqadi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Regression Problem ◽  
Simple Method ◽  
Scientific Experiments ◽  
Linear Regression Problem ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
The Relationship ◽  
Linear Regressions

<span>Multiple linear regressions are an important tool used to find the relationship between a set of variables used in various scientific experiments. In this article we are going to introduce a simple method of solving a multiple rectilinear regressions (MLR) problem that uses an artificial neural network to find the accurate and expected output from MLR problem. Different artificial neural network (ANN) types with different architecture will be tested, the error between the target outputs and the calculated ANN outputs will be investigated. A recommendation of using a certain type of ANN based on the experimental results will be raised.</span>

scholarly journals Analysing 21cm signal with artificial neural network

2017 ◽  
Vol 12 (S333) ◽  
pp. 39-42
Author(s):  
Hayato Shimabukuro ◽  
Benoit Semelin
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Power Spectrum ◽  
Machine Learning Techniques ◽  
Inversion Method ◽  
Observation Data ◽  
The Neural Network ◽  
Learning Techniques ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

AbstractThe 21cm signal at epoch of reionization (EoR) should be observed within next decade. We expect that cosmic 21cm signal at the EoR provides us both cosmological and astrophysical information. In order to extract fruitful information from observation data, we need to develop inversion method. For such a method, we introduce artificial neural network (ANN) which is one of the machine learning techniques. We apply the ANN to inversion problem to constrain astrophysical parameters from 21cm power spectrum. We train the architecture of the neural network with 70 training datasets and apply it to 54 test datasets with different value of parameters. We find that the quality of the parameter reconstruction depends on the sensitivity of the power spectrum to the different parameter sets at a given redshift and also find that the accuracy of reconstruction is improved by increasing the number of given redshifts. We conclude that the ANN is viable inversion method whose main strength is that they require a sparse extrapolation of the parameter space and thus should be usable with full simulation.

Prediction of Detonation Pressure of Aluminized Explosive by Artificial Neural Network

2013 ◽  
Vol 641-642 ◽  
pp. 460-463
Author(s):  
Yong Gang Liu ◽  
Xin Tian ◽  
Yue Qiang Jiang ◽  
Gong Bing Li ◽  
Yi Zhou Li
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Optimal Condition ◽  
Detonation Pressure ◽  
Ann Model ◽  
Training Set ◽  
The Neural Network ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Good Agreement

In this study, a three-layer artificial neural network(ANN) model was constructed to predict the detonation pressure of aluminized explosive. Elemental composition and loading density were employed as input descriptors and detonation pressure was used as output. The dataset of 41 aluminized explosives was randomly divided into a training set (30) and a prediction set (11). After optimized by adjusting various parameters, the optimal condition of the neural network was obtained. Simulated with the final optimum neural network [6–9–1], calculated detonation pressures show good agreement with experimental results. It is shown here that ANN is able to produce accurate predictions of the detonation pressure of aluminized explosive.

scholarly journals Comparison of Accuracy of Artificial Neural Network (ANN) and Kriging Methods for Estimating Chlorine Concentration

10.29007/lpmh ◽  
2018 ◽  
Author(s):  
Faezeh Ghaleh Navi ◽  
Hamed Mazandarani Zadeh ◽  
Dragan Savic
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Kriging Model ◽  
Coefficient Of Determination ◽  
Monthly Basis ◽  
Data Set ◽  
The Neural Network ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Observation Wells

Groundwater is one of the major sources of fresh water. Maintenance and management of this vital resource is so important especially in arid and semi-arid regions. Reliable and accurate groundwater quality assessment is essential as a basic data for any groundwater management studies. The aim of this study is to compare the accuracy of two Artificial Neural Network (ANN) and Kriging methods in predicting chlorine in groundwater. In case of ANN, we created an appropriate emulator, which minimize the prediction error by changing the parameters of the neural network, including the number of layers. The best Kriging model is also obtained by changing the variogram function, such that the Gaussian variogram has the least error in interpolation of the amount of chlorine. To evaluate the accuracy of these two methods, the mean square error (MSE) and Coefficient of determination (R2) are used. The data set consists of the amount of chlorine, in a monthly basis, measured at 112 observation wells from 1999 to 2015 in aquifer Qazvin, Iran. MSE values for ANN and Kriging are 14.8 and 15.4, respectively, which indicate that the ANN has a better performance and is more capable of predicting chlorine values in comparison with Kriging.

scholarly journals Classification of EMG signal for multiple hand gestures based on neural network

2020 ◽  
Vol 17 (1) ◽  
pp. 256 ◽  
Author(s):  
Mohd Azlan Abu ◽  
Syazwani Rosleesham ◽  
Mohd Zubir Suboh ◽  
Mohd Syazwan Md Yid ◽  
Zainudin Kornain ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Invasive Technique ◽  
Hand Gestures ◽  
Emg Signal ◽  
The Neural Network ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Hidden Neurons

<span>This paper presents the classification of EMG signal for multiple hand gestures based on neural network. In this study, the Electromyography is used to measure the muscle cell’s electrical activities which is commonly represented in a function time. Every muscle has their own signals, which was produced in every movement. Surface electromyography (sEMG) is used as a non-invasive technique for acquiring the EMG signal. The development of sensors’ detection and measuring the EMG have been improved and have become more precise while maintaining a small size. In this paper, the main objective is to identify the hand gestures based on: (1) Cylindrical Grasp, (2) Supination (Twist Left), (3) Pronation (Twist Right), (4) Resting Hand and (5) Open Hand that are predefined by using Arduino IDE, CoolTerm software and Microsoft Excel before using artificial neural network for classifying purposes in MATLAB. Finally, the extraction of the EMG patterns for each movement went through features extraction of the signals which is used to train the classifier in MATLAB to classify signals in the neural network. The features extracted are using mean absolute value (MAV), median, waveform length (WL) and root mean square (RMS). The Artificial Neural Network (ANN) produced accuracy of 80% for training and testing for 10 hidden neurons layer.</span>

scholarly journals P318 Artificial intelligence of the neural network-assistant for differential diagnosis of IBD

2020 ◽  
Vol 14 (Supplement_1) ◽  
pp. S313-S313
Author(s):  
M Skalinskaya ◽  
I Bakulin ◽  
E Skazyvaeva ◽  
I Rasmagina ◽  
G Mashevskii ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Differential Diagnosis ◽  
Control Sample ◽  
Image Resolution ◽  
The Neural Network ◽  
Output Neurons ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Endoscopic Picture

Abstract Background Due to the lack of a ‘gold standard’ in the diagnosis of IBD the differential diagnosis between ulcerative colitis and Crohn′s disease can be very difficult. Verification of diagnosis of IBD takes a long time in majority of cases. Methods We have created an artificial neural network (ANN) of the multilayer perceptron type using the Neural Network Toolbox application from the MATLAB application package. Three types of images were used to train the ANN: the norm of the endoscopic picture of the colon, the endoscopic pictures of UC and CD. The first stage is the training of an artificial neural network to distinguish the presence or absence of pathology (29 images of the ‘normal colon’, 14 images of the CD, and 15 - UC). The second stage was to train the ANN to recognise the various forms of IBD. The network was trained on an array of 124 images (62 images of each class of pathologies). Each image was previously converted to the grayscale mode and then into a matrix of pixels. A vector with the number of elements equal to the size of the image was fed to the input of the perceptron. Results To solve the task of identifying the pathology a perceptron was built with 32,2784 input neurons, 10 hidden neurons and 2 output neurons which represent the conclusion that the image belongs to one of the two classes: norm or pathology. To solve the problem of differentiating CD and UC a perceptron was created with 364500 input neurons (this value was determined by the image resolution) and 2 output neurons representing the conclusion that the image belongs to one of the two classes: UC or CD. The best result in differentiation of pathology was shown by the ANN of MP 364500 type: 364500-20-2: 2, which total accuracy of recognition was 96,8%. The average accuracy of the developed model was 92.6%. However, in the control sample, the accuracy was 84.2%. This fact indicates that the model should be taught on more images. In addition to the ‘accuracy’ criterion, the ‘completeness’ parameter was used to evaluate the system. ‘Completeness’ for recognition of the image of the norm was the highest and equal to 1, for UC the criterion of ‘completeness’ was 0.89. The lowest ‘completeness’ was obtained when recognising the image of the CD (0.67). Conclusion ANN type MP 364500: 364500-20-2: 2 has shown the best results in the set targets. Efficiency in pathology recognition was 96.8%. The efficiency of the created ANN in solving the problem of recognition of different forms of IBD (UC/CD) can be described by the following parameters: specificity (Sp) −78.2%, sensitivity (Se) - 93.1%, accuracy (Ac) - 85,7%. The obtained ANN can be used to solve the problems of classification of endoscopic images of the intestine for the presence of IBD and for differential diagnosis.

Prediction of Detonation Velocity of Aluminized Explosive by Artificial Neural Network

2013 ◽  
Vol 790 ◽  
pp. 673-676
Author(s):  
Yue Qiang Jiang ◽  
Yong Gang Liu ◽  
Xin Tian ◽  
Gong Bing Li
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Optimal Condition ◽  
Detonation Velocity ◽  
Ann Model ◽  
Training Set ◽  
The Neural Network ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Good Agreement

In this study, a three-layer artificial neural network (ANN) model was constructed to predict the detonation velocity of aluminized explosive. Elemental composition and loading density were employed as input descriptors and detonation velocity was used as output. The dataset of 61 aluminized explosives was randomly divided into a training set (49) and a prediction set (12). After optimized by adjusting various parameters, the optimal condition of the neural network was obtained. Simulated with the final optimum neural network [812, calculated detonation velocity show good agreement with experimental results. It is shown that ANN is able to produce accurate predictions of the detonation velocity of aluminized explosive.

A Neural Network Based Space-Vector PWM Controller for Motor Drive

2013 ◽  
Vol 816-817 ◽  
pp. 1002-1005
Author(s):  
Zhi Yuan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Integrated Circuit ◽  
Pulse Width Modulation ◽  
Motor Drive ◽  
Switching Frequency ◽  
Space Vector ◽  
The Neural Network ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

This paper proposed an artificial neural network (ANN) based space vector pulse width modulation (SVPWM) for motor drive which fully covers the undermodulation and overmodulation regions. A neural network has the advantage of very fast implementation of an SVPWM algorithm that can increase the converter switching frequency, particularly when a dedicated application-specific integrated circuit chip is used in the modulator. Finally, in the environment of MATLAB/Simulink with the Neural Network Toolbox builds the simulation model of system with proposed ANN-SVPWM controller. The simulation results show that the performances of the motor drive with artificial-neural-network-based SVPWM are excellent.

scholarly journals Creep analysis of bimaterial microcantilever beam for sensing device using artificial neural network (ANN)

2017 ◽  
Vol 23 (1&2) ◽  
pp. 89 ◽  
Author(s):  
WaiChi Wong ◽  
HingWah Lee ◽  
Ishak A. Azid ◽  
K.N. Seetharamu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Back Propagation ◽  
Feed Forward Back Propagation ◽  
The Neural Network ◽  
Creep Analysis ◽  
Simulation Results ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Microcantilever Beam

In this study, a feed-forward back-propagation Artificial Neural Network (ANN) is used to predict the stress relaxation and behavior of creep for bimaterial microcantilever beam for sensing device. Results obtained from ANSYS® 8.1 finite element (FE) simulations, which show good agreement with experimental work [1], is used to train the neural network. Parametric studies are carried out to analyze the effects of creep on the microcantilever beam in term of curvature and stress deve loped with time. It is shown that ANN accurately predicts the stress level for the microcantilever beam using the trained ANSYS® simulation results due to the fact that there is no scattered data in the FE simulation results. ANN takes a small fraction of time and effort compar ed to FE prediction.

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