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SR21 Redes Neurais, Algoritmos Genéticos e Lógicas Nebulosa/Neural |
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Title:
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AN INVERSE INITIAL CONDITION PROBLEM IN HEAT CONDUCTION: A NEURAL NETWORK APPROACH
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Summary :
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ABSTRACT: WE DETERMINE THE INITIAL TEMPERATURE PROFILE ON A SLAB WITH ADIABATIC BOUNDARY CONDITION, FROM A TRANSIENT TEMPERATURE DISTRIBUTION, OBTAINED AT A GIVEN TIME. THIS IS AN ILL-POSED 1D PARABOLIC INVERSE PROBLEM, WHERE THE INITIAL CONDITION HAS TO BE ESTIMATED. TWO DIFFERENT ARTIFICIAL NEURAL NETWORKS HAVE BEEN APPLIED TO ADDRESS THE PROBLEM: BACKPROPAGATION AND RADIAL BASIS FUNCTIONS (RBF). BOTH APPROACHES USE THE WHOLE TEMPERATURE HISTORY MAPPING. IN OUR SIMULATIONS, RBF PRESENTED BETTER SOLUTIONS, FASTER TRAINING, BUT HIGHER NOISE SENSITIVENESS, AS COMPARED TO BACKPROPAGATION.
KEY WORDS: INVERSE PROBLEMS, NEURAL NETWORKS, BACKPROPAGATION, RADIAL BASIS FUNCTIONS.
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Author :
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da Luz, Jeferson I.
Issamoto, Edison 0
Miki, Fabio Tokio
Silva, Jose Demisio S.
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Paper View :
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