relation: https://khub.utp.edu.my/scholars/980/
title: Enhanced conjugate gradient methods for training MLP-networks
creator: Izzeldin, H.
creator: Asirvadam, V.S.
creator: Saad, N.
description: The paper investigates the enhancement in various conjugate gradient training algorithms applied to a multilayer perceptron (MLP) neural network architecture. The paper investigates seven different conjugate gradient algorithms proposed by different researchers from 1952-2005, the classical batch back propagation, full-memory and memory-less BFGS (Broyden, Fletcher, Goldfarb and Shanno) algorithms. These algorithms are tested in predicting fluid height in two different control tank benchmark problems. Simulations results show that Full-Memory BFGS has overall better performance or less prediction error however it has higher memory usage and longer computational time conjugate gradients. ©2010 IEEE.
date: 2010
type: Conference or Workshop Item
type: PeerReviewed
identifier:   Izzeldin, H. and Asirvadam, V.S. and Saad, N.  (2010) Enhanced conjugate gradient methods for training MLP-networks.  In: UNSPECIFIED.     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-79951986009&doi=10.1109%2fSCORED.2010.5703989&partnerID=40&md5=abec87933aec16425408303635d9a44b
relation: 10.1109/SCORED.2010.5703989
identifier: 10.1109/SCORED.2010.5703989