scholarly journals Identification of the best hidden layer size for three-layered neural net in predicting monsoon rainfall in India

2008 ◽  
Vol 10 (2) ◽  
pp. 181-188 ◽  
Author(s):  
Surajit Chattopadhyay ◽  
Goutami Chattopadhyay
Keyword(s):  
Neural Network ◽  
Summer Monsoon ◽  
Monsoon Rainfall ◽  
Test Procedure ◽  
Network Models ◽  
Summer Monsoon Rainfall ◽  
Neural Net ◽  
Neural Network Models ◽  
Hidden Layer ◽  
Hidden Nodes

In the present research, long-range prediction of average summer monsoon rainfall over India has been attempted through three layered artificial neural network models. The study is based on the summer monsoon data pertaining to the years 1871–1999. Nineteen neural network models have been developed with variable hidden layer size. Total rainfall amounts in the summer monsoon months of a given year have been used as input and the average summer monsoon rainfall of the following year has been used as the desired output to execute a supervised backpropagation learning procedure. After a thorough training and test procedure, a neural network with eleven nodes in the hidden layer is found to be the most proficient in forecasting the average summer monsoon rainfall of a given year with the said predictors. Finally, the performance of the eleven-hidden-nodes three-layered neural network has been compared with the performance of the asymptotic regression technique. Ultimately it has been established that the eleven-hidden-nodes three-layered neural network has more efficacy than asymptotic regression in the present forecasting task.

System Identification for Nonlinear Maneuvering of Ships Using Neural Network

2010 ◽  
Vol 54 (01) ◽  
pp. 1-14
Author(s):  
G. Rajesh ◽  
G. Giri Rajasekhar ◽  
S. K. Bhattacharyya
Keyword(s):  
Neural Network ◽  
System Identification ◽  
Simulated Data ◽  
Network Models ◽  
Training Data ◽  
Neural Network Models ◽  
The Neural Network ◽  
Marquardt Algorithm ◽  
Hydrodynamic Derivatives ◽  
Hidden Layer

This paper deals with the application of nonparametric system identification to the nonlinear maneuvering of ships using neural network method. The maneuvering equations contain linear as well as nonlinear terms, and one does not attempt to determine the parameters (or hydrodynamic derivatives) associated with nonlinear terms, rather all nonlinear terms are clubbed together to form one unknown time function per equation, which are sought to be represented by neural network coefficients. The time series used in training the network are obtained from simulated data of zigzag and spiral maneuvers. The neural network has one middle or hidden layer of neurons and the Levenberg-Marquardt algorithm is used to obtain the network coefficients. Using the best choices for number of hidden layer neurons, length of training data, convergence tolerance, and so forth, the performances of the proposed neural network models have been investigated and conclusions drawn.

scholarly journals Prediction of Area and Production of Groundnut Using Box-Jenkins Arima and Neural Network Approach

2020 ◽  
Author(s):  
S. T. Pavana Kumar ◽  
Ferdinand B. Lyngdoh
Keyword(s):  
Neural Network ◽  
Moving Average ◽  
Arima Model ◽  
Network Models ◽  
Arima Models ◽  
Neural Network Models ◽  
Neural Network Approach ◽  
Data Set ◽  
The Neural Network ◽  
Hidden Layer

Selection of parameters for Auto Regressive Integrated Moving Average (ARIMA) model in the prediction process is one of the most important tasks. In the present study, groundnut data was utlised to decide appropriate p, d, q parameters for ARIMA model for the prediction purpose. Firstly, the models were fit to data without splitting into training and validation/testing sets and evaluated for their efficiency in predicting the area and production of groundnut over the years. Meanwhile, models are compared among other fitted ARIMA models with different p, d, q parameters based on decision criteria’s viz., ME, RMSE, MAPE, AIC, BIC and R-Square. The ARIMA model with parameters p-2 d-1-2, q-1-2 are found adequate in predicting the area as well as production of groundnut. The model ARIMA (2, 2, 2) and ARIMA (2,1,1) predicted the area of groundnut crop with minimum error estimates and residual characteristics (ei). The models were fit into split data i.e., training and test data set, but these models’ prediction power (R-Square) declined during testing. In case of predicting the area, ARIMA (2,2,2) was consistent over the split data but it was not consistent while predicting the production over years. Feed-forward neural networks with single hidden layer were fit to complete, training and split data. The neural network models provided better estimates compared to Box-Jenkins ARIMA models. The data was analysed using R-Studio.

scholarly journals Artificial Intelligent techniques for Flow Bottom Hole Pressure Prediction

2016 ◽  
Vol 15 (12) ◽  
pp. 7263-7283
Author(s):  
M Awadalla ◽  
H Yousef ◽  
A Al-Shidani ◽  
A Al-Hinai
Keyword(s):  
Neural Network ◽  
Mean Square Error ◽  
Correlation Coefficient ◽  
Network Models ◽  
Data Sets ◽  
Mean Square ◽  
Layer Model ◽  
Neural Network Models ◽  
Bottom Hole ◽  
Hidden Layer

This paper proposes Radial Basis and Feed-forward Neural Networks to predict the flowing bottom-hole pressure in vertical oil wells. The developed neural network models rely on a large amount of available historical data measured from actual different oil fields. The unsurpassed number of neural network layers, the number of neurons per layer, and the number of trained samples required to get an outstanding performance have been obtained. Intensive experiments have been conducted and the standard statistical analysis has been  accomplished on the achieved results to validate the models’ prediction accuracy. For the sake of qualitative comparison, empirical modes have been developed. The obtained results show that the proposed Feed-Forward Neural Network models outperforms and capable of estimating the FBHPaccurately.The paper showed that the accuracy of FBHP estimation using FFNN with two hidden layer model is better than FFNN with single hidden layer model, Radial Basis neural network, and the empirical model in terms of data set used, mean square error, and the correlation coefficient error. With best results of 1.4 root mean square error (RMSE), 1.4 standard deviation of relative error (STD), correlation coefficient (R) 1.0 and 99.4% of the test data sets achieved less than 5% error. The minimum sufficient number of data sets used in training ANN model can be low as 375 sets only to give a 3.4  RMES and 97% of the test data achieved 90% accuracy.

scholarly journals Credit Spreads And Systematic RiskIn The U.S. Banking Industry - A Neural Network Model Approach

2015 ◽  
Vol 31 (5) ◽  
pp. 1799
Author(s):  
Mark A. Anderson ◽  
Frantz Maurer
Keyword(s):  
Neural Network ◽  
Banking Industry ◽  
Systematic Risk ◽  
Network Models ◽  
Credit Spread ◽  
Credit Spreads ◽  
Statistical Dependence ◽  
Neural Network Models ◽  
Hidden Layer ◽  
The U.S

<p>This paper shows that systematic risk in the U.S. banking industry displayed historical responsiveness to variations in the AAA-Baa credit spread. Critically, through the development of a series of single hidden layer perceptron neural network models, the principal credit spreads in the fixed income market catalyzed a defined regime shift in systematic risk proximate the financial crisis, and was more influential to the quantification of realized systematic risk than the statistical specifications of beta. As an intriguing result of the learned model simulations, the beta slope coefficients for the largest banks in the study exhibited significant acceleration in the statistical dependence on credit spread variations.</p>

Neural Network Models of Religious Belief

1995 ◽  
Vol 38 (4) ◽  
pp. 483-495 ◽  
Author(s):  
William Sims Bainbridge
Keyword(s):  
Neural Network ◽  
New Technology ◽  
Network Models ◽  
Eternal Life ◽  
Neural Net ◽  
Neural Network Models ◽  
Social Scientists ◽  
Theory Of Religion ◽  
Wide Range ◽  
Neural Network Technology

This paper applies neural network technology, a standard approach in computer science that has been unaccountably ignored by sociologists, to the problem of developing rigorous sociological theories. A simulation program employing a “varimax” model of human learning and decision-making models central elements of the Stark-Bainbridge theory of religion. Individuals in a micro-society of 24 simulated people learn which categories of potential exchange partners to seek for each of four material rewards which in fact can be provided by other actors in the society. However, when they seek eternal life, they are unable to find suitable human exchange partners who can provide it to them, so they postulate the existence of supernatural exchange partners as substitutes. The explanation of how the particular neural net works, including reference to modulo arithmetic, introduces some aspects of this new technology to sociology, and this paper invites readers to explore the wide range of other neural net techniques that may be of value for social scientists

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