• Optimization of nonlinear geol...

Result: Optimization of nonlinear geological structure mapping using hybrid neuro-genetic techniques

Title:
Optimization of nonlinear geological structure mapping using hybrid neuro-genetic techniques
Authors:
GANESAN, T, VASANT, P, ELAMVAZUTHI, I
Source:
Mathematical and computer modelling. 54(11-12):2913-2922
Publisher Information:
Kidlington: Elsevier, 2011.
Publication Year:
2011
Physical Description:
print, 28 ref
Original Material:
INIST-CNRS
Subject Terms:
Computer science, Informatique, Mathematics, Mathématiques, Sciences exactes et technologie, Exact sciences and technology, Sciences et techniques communes, Sciences and techniques of general use, Mathematiques, Mathematics, Logique mathématique, fondements, théorie des ensembles, Mathematical logic, foundations, set theory, Logique et fondements, Logic and foundations, Logique générale, General logic, Analyse mathématique, Mathematical analysis, Calcul des variations et contrôle optimal, Calculus of variations and optimal control, Analyse numérique. Calcul scientifique, Numerical analysis. Scientific computation, Analyse numérique, Numerical analysis, Méthodes numériques en programmation mathématique, optimisation et calcul variationnel, Numerical methods in mathematical programming, optimization and calculus of variations, Optimisation et calcul variationnel numériques, Numerical methods in optimization and calculus of variations, Méthodes de calcul scientifique (y compris calcul symbolique, calcul algébrique), Methods of scientific computing (including symbolic computation, algebraic computation), Algorithme génétique, Genetic algorithm, Algoritmo genético, Analyse assistée, Computer aided analysis, Análisis asistido, Analyse numérique, Numerical analysis, Análisis numérico, Calcul variationnel, Variational calculus, Cálculo de variaciones, Etude comparative, Comparative study, Estudio comparativo, Logique floue, Fuzzy logic, Lógica difusa, Mathématiques appliquées, Applied mathematics, Matemáticas aplicadas, Minimisation fonction, Function minimization, Minimización función, Modèle mathématique, Mathematical model, Modelo matemático, Méthode optimisation, Optimization method, Método optimización, Problème non linéaire, Nonlinear problems, Programmation mathématique, Mathematical programming, Programación matemática, Réseau neuronal, Neural network, Red neuronal, 03B52, 49XX, 65K10, 65Kxx, Engineering problems, Genetic programming, Geological structure mapping, Hybrid optimization, Neuro-genetic programming, Nonlinear
Document Type:
Academic journal Article
File Description:
text
Language:
English
Author Affiliations:
Mechanical Engineering Department, University Technology Petronas, Malaysia
Fundamental and Applied Sciences Department, University Technology Petronas, Malaysia
Electrical & Electronic Engineering Department, University Technology Petronas, Malaysia
ISSN:
0895-7177
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=24559725
Rights:
Copyright 2015 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Mathematics
Accession Number:
edscal.24559725
Database:
PASCAL Archive

Further Information

A fairly reasonable result was obtained for nonlinear engineering problems using the optimization techniques such as neural network, genetic algorithms, and fuzzy logic independently in the past. Increasingly, hybrid techniques are being used to solve the nonlinear problems to obtain a better output. This paper discusses the use of neuro-genetic hybrid technique to optimize the geological structure mapping which is known as seismic survey. It involves minimization of objective function subject to the requirement of geophysical and operational constraints. In this work, the optimization was initially performed using genetic programming, and followed by hybrid neuro-genetic programming approaches. Comparative studies and analysis were then carried out on the optimized results. The results indicate that the hybrid neuro-genetic hybrid technique produced better results compared to the stand-alone genetic programming method.