TY  - CONF
KW  - Mean square error; Network layers; Regression analysis
KW  -  Airwaves; Coefficient of determination; Controlled Source Electro-Magnetic; Multi-layer perceptron neural networks; Multiple regrssion; Shallow waters
KW  -  Multilayer neural networks
TI  - Airwaves estimation in shallow water CSEM data: Multi-layer perceptron versus multiple regression
Y1  - 2014///
SN  - 9781479943913
N2  - In this study, a Multi-Layer Perceptron Neural Network and Multiple Regression techniques are used to estimate airwaves associated with shallow water Controlled-Source Electro-Magnetic (CSEM) data. Both techniques are appropriate for the development of estimation models. However, multiple regression models make some assumptions about the underlying data. These assumptions include independence, normality and homogeneity of variance. Conversely, neural network based models are not constrained by such assumptions. The performance of the two techniques is calculated based on coefficient of determination (R2) and mean square error (MSE). The results indicate that MLP produced better estimate for the airwaves with MSE of 0.0113 and R2 of 0.9935. © 2014 IEEE.
ID  - scholars4268
A1  - Abdulkarim, M.
A1  - Ahmad, W.F.W.
A1  - Ansari, A.
A1  - Nyamasvisva, E.T.
A1  - Shafie, A.
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-84938772012&doi=10.1109%2fICCOINS.2014.6868367&partnerID=40&md5=3ac022e89cf2d912ca20f73818e47b26
N1  - cited By 0; Conference of 2014 International Conference on Computer and Information Sciences, ICCOINS 2014 ; Conference Date: 3 June 2014 Through 5 June 2014; Conference Code:112912
AV  - none
PB  - Institute of Electrical and Electronics Engineers Inc.
ER  -