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 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.

scholarly journals Prediction of water discharge and sediment in teak forested area using artificial neural network model

2017 ◽  
Vol 6 (1) ◽  
pp. 73
Author(s):  
Nining Wahyuningrum
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Water Discharge ◽  
Ann Modeling ◽  
Highly Nonlinear ◽  
Condition Modeling ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Relationship Of ◽  
The Relationship

Information on the relationship of rainfall with discharge and sediment are required in watershed management.This relationship is known to be highly nonlinear and complex. Although discharge and sediment has been monitored continuously, but sometimes the information is not or less complete. In this condition, modeling is indispensable.The research objective is to create a model to predict the monthly direct runoff and sediment using Artificial Neural Network (ANN).The model was tested using rainfall data at t-3 and t-4 as input, and discharge and sediment at t+3 and t+4 as output. The data used is the data from 2001 to 2014. The results showed that of some models tested there are two models for the prediction of discharge and two models for sediment.The model was chosen because it has the smallest MSE, the largest R2 and satisfying K (0.5 to 0.65).Thus, these models can be used to predict discharge andsediment for a period of t+3 and t+4. Prediction of discharge of t+3 and t+4 may use Q t+3 = 0,64 Q t-3 + 0,05 and Q t+4 = 0,65 Q t-4 + 0,074 res pectively, while for predicting sediment of t+3 and t+4 may use equations QS t+3 = 0,45 QS t-3 + 0,052 and QS t+4 = 0,45 QS t-4 + 0,052. This ANN modeling can be applied to predict the flow and sediment in other locations with an architecture adapted to the conditions of available data.

scholarly journals The use of artificial neural network and multiple linear regressions for stock market forecasting

MATEMATIKA ◽  
2017 ◽  
Vol 33 (1) ◽  
pp. 1
Author(s):  
Abdu Masanawa Sagir ◽  
Saratha Sathasivan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Stock Exchange ◽  
Multiple Linear Regressions ◽  
Ann Model ◽  
Network Parameter ◽  
Stock Market Forecasting ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Linear Regressions

In the recent economic crises, one of the precise uniqueness that all stock markets have in common is the uncertainty. An attempt was made to forecast future index of the Malaysia Stock Exchange Market using artificial neural network (ANN) model and a traditional forecasting tool – Multiple Linear Regressions (MLR). This paper starts with a brief introduction of stock exchange of Malaysia, an overview of artificial neural network and machine learning models used for prediction. System design and data normalization using MINITAB software were described. Training algorithm, MLR Model and network parameter models were presented. Best training graphs showing the training, validation, test and all regression values were analyzed.

scholarly journals Monitoring of Preload Variation of Linear Guide Positioning Stage Using Artificial Neural Network

2021 ◽  
Vol 11 (17) ◽  
pp. 7902
Author(s):  
Wei-Cheng Shih ◽  
Furqan Furqanuddin ◽  
Po-Lin Lee ◽  
Jui-Pin Hung
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Contact Stiffness ◽  
Feeding Rates ◽  
Linear Guide ◽  
Average Percentage ◽  
Ann Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
The Relationship

In this paper, we propose an artificial neural network (ANN) predictive model to identify the linear guide preload based on the measured vibration features of the feeding stage. In this study, the relationship between the contact stiffness and preload level of a linear guide was investigated by an experimental analysis. Furthermore, the stage was assembled with different linear guide preloads for the motion test to assess the vibrations. Vibration levels with changes in preload values and feeding rates were examined. The predictive models were established and verified based on a dataset collected from tests using an ANN approach. The ANN models were shown to have an excellent accuracy of 96.5% in the training datasets, which were collected from stages with sliding blocks rated at consistent preloads. The average percentage prediction error in the verification dataset was approximately 8.54–11.23%. This is probably because the stage with an unevenly distributed preload in the sliding blocks induces vibration with more fluctuation, which eventually affects the prediction accuracy. The results verify the feasibility of online preload identification for the condition monitoring of the feeding system.

Application of the Artificial Neural Network (ANN) Method as MPPT Photovoltaic for DC Source Storage

2019 ◽  
Vol 12 (3) ◽  
pp. 145 ◽  
Author(s):  
Epyk Sunarno ◽  
Ramadhan Bilal Assidiq ◽  
Syechu Dwitya Nugraha ◽  
Indhana Sudiharto ◽  
Ony Asrarul Qudsi ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Random Forest (RF) and Artificial Neural Network (ANN) Algorithms for LULC Mapping

2020 ◽  
Vol 38 (4A) ◽  
pp. 510-514
Author(s):  
Tay H. Shihab ◽  
Amjed N. Al-Hameedawi ◽  
Ammar M. Hamza
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Artificial Neural Network ◽  
Random Forest ◽  
Satellite Image ◽  
Landsat 8 ◽  
Optical Remote Sensing ◽  
Data Set ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this paper to make use of complementary potential in the mapping of LULC spatial data is acquired from LandSat 8 OLI sensor images are taken in 2019.  They have been rectified, enhanced and then classified according to Random forest (RF) and artificial neural network (ANN) methods. Optical remote sensing images have been used to get information on the status of LULC classification, and extraction details. The classification of both satellite image types is used to extract features and to analyse LULC of the study area. The results of the classification showed that the artificial neural network method outperforms the random forest method. The required image processing has been made for Optical Remote Sensing Data to be used in LULC mapping, include the geometric correction, Image Enhancements, The overall accuracy when using the ANN methods 0.91 and the kappa accuracy was found 0.89 for the training data set. While the overall accuracy and the kappa accuracy of the test dataset were found 0.89 and 0.87 respectively.

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