scholarly journals Preliminary Researches Regarding the Use of Ann to Predict the Wheel-Soil Interaction

2013 ◽  
Vol 46 (1) ◽  
pp. 5-13
Author(s):  
H. Taghavifar ◽  
A. Mardani ◽  
I. Elahi
Keyword(s):  
Contact Area ◽  
Mean Squared Error ◽  
Back Propagation ◽  
Ann Model ◽  
Back Propagation Algorithm ◽  
Feed Forward Neural Network ◽  
Complex Relation ◽  
Squared Error ◽  
Conventional Models ◽  
Complex Relationships

Abstract Soil-wheel interactions as a phenomenon in which both components are behaving nonlinearly has been considered a sophisticated and complex relation to be modeled. A well-trained artificial neural networks as a useful tool is widely used in variety of science and engineering fields. We inspired to use this facility for application of some soil-wheel interaction products since nonlinear and complex relationships between wheel and soil necessitate more precise and reliable calculations. A 2-14-2 feed forward neural network with back propagation algorithm was found to have acceptable performance with mean squared error of 0.020. This model was used to predict two output variables of rut depth and contact area with regression correlations of 0.99961 and 0.99996 for rut depth and contact area, respectively. Furthermore, the results were compared with conventional models proposed for predicting the contact area and rut depth. The promising results of ANN model give higher privilege over conventional models. The findings also introduce the potential of ANN for modeling. However, the authors recommend further studies to be conducted in this realm of computing due to its great potential and capability.

Lightweight Concrete Strength Prediction by BP-ANN

2015 ◽  
Vol 1090 ◽  
pp. 101-106
Author(s):  
Kai Huang ◽  
Cheng Wei Zhong
Keyword(s):  
Lightweight Concrete ◽  
Concrete Strength ◽  
Back Propagation ◽  
Strength Prediction ◽  
Test Results ◽  
Ann Model ◽  
Back Propagation Algorithm ◽  
Feed Forward Neural Network ◽  
Technical Literature ◽  
Model Based

The back propagation artificial neural networks (BP-ANN) use a resilient back-propagation algorithm and early stopping technique. By inputing the properties of geometries and material, NNs can predict the strength of lightweight concrete. An BP-ANN model based on feed-forward neural network is built, trained and tested using the available test data of 148 mix records collected from the technical literature. And the test results are compared and analyzed with experimental data . It shows that the strength of lightweight concrete obtained by the simplified model based on NNs are in good agreement with test results, and they are close to the experimental values. The NNs model can be used in the shear strength prediction and design for the strength of lightweight concrete.

PREDICTING THE RHEOLOGICAL PROPERTIES OF BITUMEN-FILLER MASTIC USING ARTIFICIAL NEURAL NETWORK METHODS

2017 ◽  
Vol 80 (1) ◽  
Author(s):  
Nursyahirah Khamis ◽  
Muhamad Razuhanafi Mat Yazid ◽  
Asmah Hamim ◽  
Sri Atmaja P. Rosyidi ◽  
Nur Izzi Md. Yusoff ◽  
...  
Keyword(s):  
Neural Network ◽  
Rheological Properties ◽  
Network Model ◽  
Mean Squared Error ◽  
Complex Modulus ◽  
Radial Basis Function Network ◽  
Ann Model ◽  
Feed Forward Neural Network ◽  
Squared Error ◽  
Function Network

This study was conducted to develop two types of artificial neural network (ANN) model to predict the rheological properties of bitumen-filler mastic in terms of the complex modulus and phase angle. Two types of ANN models were developed namely; (i) a multilayer feed-forward neural network model and (ii) a radial basis function network model. This study was also conducted to evaluate the accuracy of both types of models in predicting the rheological properties of bitumen-filler mastics by means of statistical parameters such as the coefficient of determination (R2), mean absolute error (MAE), mean squared error (MSE) and root mean squared error (RMSE) for every developed model. A set of dynamic shear rheometer (DSR) test data was used on a range of the bitumen-filler mastics with three filler types (limestone, cement and grit stone) and two filler concentrations (35 and 65% by mass). Based on the analysis performed, it was found that both models were able to predict the complex modulus and phase angle of bitumen-filler mastics with the average R2 value exceeding 0.98. A comparison between the two types of models showed that the radial basis function network model has a higher accuracy than multilayer feed-forward neural network model with a higher value of R2 and lower value of MAE, MSE and RMSE. It can be concluded that the ANN model can be used as an alternative method to predict the rheological properties of bitumen-filler mastic. 

scholarly journals Optimization Forecasting using Back-Propagation Algorithm

2021 ◽  
Author(s):  
Budi Raharjo ◽  
Nurul Farida ◽  
Purwo Subekti ◽  
Rima Herlina S Siburian ◽  
Putu Doddy Heka Ardana ◽  
...  
Keyword(s):  
Mean Squared Error ◽  
Animal Health ◽  
Back Propagation ◽  
Learning Rate ◽  
Propagation Model ◽  
Training Data ◽  
Back Propagation Algorithm ◽  
Root Mean Squared Error ◽  
Squared Error ◽  
Testing Data

The purpose of this study was to evaluate the back-propagation model by optimizing the parameters for the prediction of broiler chicken populations by provinces in Indonesia. Parameter optimization is changing the learning rate (lr) of the backpropagation prediction model. Data sourced from the Directorate General of Animal Husbandry and Animal Health processed by the Central Statistics Agency (BPS). Data is the population of Broiler Chickens from 2017 to 2019 (34 records). The analysis process uses the help of RapidMiner software. Data is divided into 2 parts, namely training data (2017-2018) and testing data (2018-2019). The backpropagation model used is 1-2-1; 1-25-1 and 1-45-1 with a learning rate (0.1; 0.01; 0.001; 0.2; 0.02; 0.002; 0.3; 0.03; 0.003). From the three models tested, the 1-45-1 model (lr = 0.3) is the best model with Root Mean Squared Error = 0.028 in the training data. With this model, the prediction results obtained with an accuracy value of 91% and Root Mean Squared Error = 0.00555 in the testing data

scholarly journals Using Artificial Neural Networks in Predicting the Level of Stress among Military Conscripts

Mathematics ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 626
Author(s):  
Svajone Bekesiene ◽  
Rasa Smaliukiene ◽  
Ramute Vaicaitiene
Keyword(s):  
Neural Network ◽  
Transfer Functions ◽  
Back Propagation ◽  
Armed Forces ◽  
Activation Function ◽  
Optimal Number ◽  
Ann Model ◽  
Back Propagation Algorithm ◽  
Classification Rate ◽  
Artificial Neural

The present study aims to elucidate the main variables that increase the level of stress at the beginning of military conscription service using an artificial neural network (ANN)-based prediction model. Random sample data were obtained from one battalion of the Lithuanian Armed Forces, and a survey was conducted to generate data for the training and testing of the ANN models. Using nonlinearity in stress research, numerous ANN structures were constructed and verified to limit the optimal number of neurons, hidden layers, and transfer functions. The highest accuracy was obtained by the multilayer perceptron neural network (MLPNN) with a 6-2-2 partition. A standardized rescaling method was used for covariates. For the activation function, the hyperbolic tangent was used with 20 units in one hidden layer as well as the back-propagation algorithm. The best ANN model was determined as the model that showed the smallest cross-entropy error, the correct classification rate, and the area under the ROC curve. These findings show, with high precision, that cohesion in a team and adaptation to military routines are two critical elements that have the greatest impact on the stress level of conscripts.

AN ARTIFICIAL INTELLIGENT APPROACH TO TRAFFIC ACCIDENT ESTIMATION: MODEL DEVELOPMENT AND APPLICATION

Transport ◽  
2009 ◽  
Vol 24 (2) ◽  
pp. 135-142 ◽  
Author(s):  
Ali Payıdar Akgüngör ◽  
Erdem Doğan
Keyword(s):  
Genetic Algorithm ◽  
Model Development ◽  
Back Propagation ◽  
Linear Functions ◽  
Model Parameters ◽  
Ann Model ◽  
Back Propagation Algorithm ◽  
Estimation Model ◽  
Average Growth ◽  
Feed Forward Back Propagation

This study proposes an Artificial Neural Network (ANN) model and a Genetic Algorithm (GA) model to estimate the number of accidents (A), fatalities (F) and injuries (I) in Ankara, Turkey, utilizing the data obtained between 1986 and 2005. For model development, the number of vehicles (N), fatalities, injuries, accidents and population (P) were selected as model parameters. In the ANN model, the sigmoid and linear functions were used as activation functions with the feed forward‐back propagation algorithm. In the GA approach, two forms of genetic algorithm models including a linear and an exponential form of mathematical expressions were developed. The results of the GA model showed that the exponential model form was suitable to estimate the number of accidents and fatalities while the linear form was the most appropriate for predicting the number of injuries. The best fit model with the lowest mean absolute errors (MAE) between the observed and estimated values is selected for future estimations. The comparison of the model results indicated that the performance of the ANN model was better than that of the GA model. To investigate the performance of the ANN model for future estimations, a fifteen year period from 2006 to 2020 with two possible scenarios was employed. In the first scenario, the annual average growth rates of population and the number of vehicles are assumed to be 2.0 % and 7.5%, respectively. In the second scenario, the average number of vehicles per capita is assumed to reach 0.60, which represents approximately two and a half‐fold increase in fifteen years. The results obtained from both scenarios reveal the suitability of the current methods for road safety applications.

scholarly journals Spatial Variability of Aroma Profiles of Cocoa Trees Obtained through Computer Vision and Machine Learning Modelling: A Cover Photography and High Spatial Remote Sensing Application

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3054 ◽  
Author(s):  
Sigfredo Fuentes ◽  
Gabriela Chacon ◽  
Damir D. Torrico ◽  
Andrea Zarate ◽  
Claudia Gonzalez Viejo
Keyword(s):  
Management Practices ◽  
Mean Squared Error ◽  
Computer Application ◽  
Aerial Image ◽  
Canopy Architecture ◽  
Ann Model ◽  
Aroma Profile ◽  
Squared Error ◽  
Artificial Neural Network Ann ◽  
The Tropics

Cocoa is an important commodity crop, not only to produce chocolate, one of the most complex products from the sensory perspective, but one that commonly grows in developing countries close to the tropics. This paper presents novel techniques applied using cover photography and a novel computer application (VitiCanopy) to assess the canopy architecture of cocoa trees in a commercial plantation in Queensland, Australia. From the cocoa trees monitored, pod samples were collected, fermented, dried, and ground to obtain the aroma profile per tree using gas chromatography. The canopy architecture data were used as inputs in an artificial neural network (ANN) algorithm, with the aroma profile, considering six main aromas, as targets. The ANN model rendered high accuracy (correlation coefficient (R) = 0.82; mean squared error (MSE) = 0.09) with no overfitting. The model was then applied to an aerial image of the whole cocoa field studied to produce canopy vigor, and aroma profile maps up to the tree-by-tree scale. The tool developed could significantly aid the canopy management practices in cocoa trees, which have a direct effect on cocoa quality.

scholarly journals Development of combined back propagation algorithm and radial basis function for diagnosing depression patients

2015 ◽  
Vol 4 (1) ◽  
pp. 244
Author(s):  
Bhuvana R. ◽  
Purushothaman S. ◽  
Rajeswari R. ◽  
Balaji R.G.
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Mean Squared Error ◽  
Back Propagation ◽  
Optimum Number ◽  
Back Propagation Algorithm ◽  
Efficient System ◽  
Propagation Algorithm ◽  
Radial Basis

Depression is a severe and well-known public health challenge. Depression is one of the most common psychological problems affecting nearly everyone either personally or through a family member. This paper proposes neural network algorithm for faster learning of depression data and classifying the depression. Implementation of neural networks methods for depression data mining using Back Propagation Algorithm (BPA) and Radial Basis Function (RBF) are presented. Experimental data were collected with 21 depression variables used as inputs for artificial neural network (ANN) and one desired category of depression as the output variable for training and testing proposed BPA/RBF algorithms. Using the data collected, the training patterns, and test patterns are obtained. The input patterns are pre-processed and presented to the input layer of BPA/RBF. The optimum number of nodes required in the hidden layer of BPA/RBF is obtained, based on the change in the mean squared error dynamically, during the successive sets of iterations. The output of BPA is given as input to RBF. Through the combined topology, the work proves to be an efficient system for diagnosis of depression.

Using BPNN to Predict Number of Low-Income Households in Taiwan Based on Seasonally Adjusted Annualized Rates for Real GDP

2014 ◽  
Vol 1044-1045 ◽  
pp. 1824-1827
Author(s):  
Yi Ti Tung ◽  
Tzu Yi Pai
Keyword(s):  
Neural Network ◽  
Low Income ◽  
Mean Squared Error ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Percentage Error ◽  
Real Gdp ◽  
Squared Error ◽  
Input Variables ◽  
Low Income Households

In this study, the back-propagation neural network (BPNN) was used to predict the number of low-income households (NLIH) in Taiwan, taking the seasonally adjusted annualized rates (SAAR) for real gross domestic product (GDP) as input variables. The results indicated that the lowest mean absolute percentage error (MAPE), mean squared error (MSE), root mean squared error (RMSE), and highest correlation coefficient (R) for training and testing were 4.759 % versus 19.343 %, 24429972.268 versus 781839890.859, 4942.669 versus 27961.400, and 0.945 versus 0.838, respectively.

scholarly journals Modeling of Speech Recognition Using Artificial Neural Network

2019 ◽  
Vol 8 (9) ◽  
pp. 1225-1229
Keyword(s):  
Neural Network ◽  
Speech Recognition ◽  
Pattern Matching ◽  
Back Propagation ◽  
Experimental Result ◽  
Communication Link ◽  
Back Propagation Algorithm ◽  
Feed Forward Neural Network ◽  
The Voice

Automatic speech recognition has attained a lot of significance as it can act as easy communication link between machines and humans. This mode of communication is easy for man to use as it is effortless and easy. Many approaches for extraction of the features of the speech and classification of speech have been considered. This paper unveils the importance of neutral network and the way it can be used for recognition of speech. Mel Frequency Cepstrum Coefficients is made use of for extraction of the features from the voice. For pattern matching neural network has been used. MATLAB has been used to show how the speech is recognized. In this paper the speech recognition has been done firstly by multilayer feed forward neural network using Back propagation algorithm. Then the process of speech recognition is shown by using Radial basis function neural network. The paper then analyzes the performance of both the algorithms and experimental result shows that BPNN outperforms the RBFNN.

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