relation: https://khub.utp.edu.my/scholars/1811/
title: Comparison on performance of radial basis function neural network and discriminant function in classification of CSEM data
creator: Abdulkarim, M.
creator: Shafie, A.
creator: Razali, R.
creator: Wan Ahmad, W.F.
creator: Arif, A.
description: Classification of Controlled Source Electro-Magnetic data into dichotomous groups based on the observed resistivity contrast measures is presented. These classifications may indicate the possible presence of hydrocarbon reservoir. Performance of Radial Basis Function of Neural network and Discriminant Function models were analyzed in this study. Both model's classification accuracy, Sensitivity and Specificity are compared and reported. Gaussian basis function was used for the hidden units in the RBF neural network, while quadratic form is used for the discriminant functions. The Controlled Source Electro-Magnetic data used for this study were obtained from simulating two known categories of data with and without hydrocarbon using COMSOL Multiphysics simulation software. The preliminary result indicates that the radial basis function neural network display superior accuracy, sensitivity and specificity in classifying CSEM data when compared to discriminant functions model. © 2011 Springer-Verlag.
date: 2011
type: Article
type: PeerReviewed
identifier:   Abdulkarim, M. and Shafie, A. and Razali, R. and Wan Ahmad, W.F. and Arif, A.  (2011) Comparison on performance of radial basis function neural network and discriminant function in classification of CSEM data.  Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7066 L (PART 1).  pp. 113-124.  ISSN 03029743     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-81255205115&doi=10.1007%2f978-3-642-25191-7_12&partnerID=40&md5=1f06ed7a69639b0138fc69b5051d4aeb
relation: 10.1007/978-3-642-25191-7₁₂
identifier: 10.1007/978-3-642-25191-7₁₂