eprintid: 3095
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/30/95
datestamp: 2023-11-09 15:51:21
lastmod: 2023-11-09 15:51:21
status_changed: 2023-11-09 15:44:58
type: article
metadata_visibility: show
creators_name: Asirvadam, V.S.
creators_name: Izzeldin, H.T.A.
creators_name: Saad, N.
creators_name: Mcloone, S.F.
title: Semi batch learning with store management using enhanced conjugate gradient
ispublished: pub
keywords: Conjugate gradient; Convergence performance; Correlated data; Data store; Distance measure; Higher order; Learning strategy; multilayer perceptron; Multilayer perceptron neural networks; Prediction errors; Semi-batch; Sliding-window; sliding-window learning; Store management, Backpropagation; Conjugate gradient method; Covariance matrix; Electronics engineering; Electronics industry; Industrial applications; Information technology; Learning algorithms; Multilayers; Neural networks, Information management
note: cited By 0; Conference of 2nd International Conference of Electrical and Electronics Engineering, ICEEE 2011 ; Conference Date: 1 December 2011 Through 2 December 2011; Conference Code:88163
abstract: This paper explores the performance of sliding-window based training, termed as semi batch, using multilayer perceptron (MLP) neural network in the presence of correlated data. The sliding window training is a form of higher order instantaneous learning strategy without the need of covariance matrix, usually employed for modeling and tracking purposes. Sliding-window framework is implemented to combine the robustness of offline learning algorithms with the ability to track online the underlying process of a function. This paper adopted sliding window training with recent advances in conjugate gradient direction with application of data store management e.g. simple distance measure, angle evaluation and the novel prediction error test. The simulation results show the best convergence performance is gained by using store management techniques. © 2012 Springer-Verlag.
date: 2012
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84856034373&doi=10.1007%2f978-3-642-26001-8_9&partnerID=40&md5=5be7a75695b28fc4461cb92d0d0e7a84
id_number: 10.1007/978-3-642-26001-8₉
full_text_status: none
publication: Lecture Notes in Electrical Engineering
volume: 136 LN
place_of_pub: Macau
pagerange: 61-67
refereed: TRUE
isbn: 9783642260001
issn: 18761100
citation:   Asirvadam, V.S. and Izzeldin, H.T.A. and Saad, N. and Mcloone, S.F.  (2012) Semi batch learning with store management using enhanced conjugate gradient.  Lecture Notes in Electrical Engineering, 136 LN.  pp. 61-67.  ISSN 18761100