TY  - CONF
ID  - scholars980
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-79951986009&doi=10.1109%2fSCORED.2010.5703989&partnerID=40&md5=abec87933aec16425408303635d9a44b
A1  - Izzeldin, H.
A1  - Asirvadam, V.S.
A1  - Saad, N.
N1  - cited By 4; Conference of 2010 8th IEEE Student Conference on Research and Development - Engineering: Innovation and Beyond, SCOReD 2010 ; Conference Date: 13 December 2010 Through 14 December 2010; Conference Code:83885
EP  - 143
CY  - Kuala Lumpur
SN  - 9781424486489
N2  - 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.
TI  - Enhanced conjugate gradient methods for training MLP-networks
KW  - Bench-mark problems; Broyden; Computational time; Conjugate gradient; Conjugate gradient algorithms; Memory usage; Multilayer perceptron neural networks; Offline learning; Prediction errors; Training algorithms
KW  -  Algorithms; Conjugate gradient method; Engineering research; Innovation; Network architecture
KW  -  Neural networks
Y1  - 2010///
SP  - 139
AV  - none
ER  -