eprintid: 11195
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/11/95
datestamp: 2023-11-10 03:25:43
lastmod: 2023-11-10 03:25:43
status_changed: 2023-11-10 01:14:41
type: article
metadata_visibility: show
creators_name: Bala, A.
creators_name: Ismail, I.
creators_name: Ibrahim, R.
creators_name: Sait, S.M.
creators_name: Salami, H.O.
title: Prediction Using Cuckoo Search Optimized Echo State Network
ispublished: pub
note: cited By 9
abstract: The advent of internet of things has brought a revolution in the amount of data generated in industry. Researchers now have to develop ways to harness such huge amount of data. Thus, a new method called �predictive maintenance� was developed. In this technique, sensor data is used to predict failures so that appropriate actions are taken to save accidents and costs. Artificial neural networks have proven to be excellent tools for prediction. In this work, the echo state network (ESN), which is a new concept of recurrent neural network (RNN), is used to predict failures in turbofan engines. The ESN was developed to solve the complexities of earlier RNNs. However, choosing the right topology and parameters for the ESN is often a difficult problem. Hence, we develop a cuckoo search optimization-based algorithm to optimize the ESN. The approach is compared with three particle swarm optimization methods and two other methods, and it performed better. © 2019, King Fahd University of Petroleum & Minerals.
date: 2019
publisher: Springer Verlag
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85068988259&doi=10.1007%2fs13369-019-04008-0&partnerID=40&md5=cfbe65087f6c7b8ce07944f84c3ae7e3
id_number: 10.1007/s13369-019-04008-0
full_text_status: none
publication: Arabian Journal for Science and Engineering
volume: 44
number: 11
pagerange: 9769-9778
refereed: TRUE
issn: 2193567X
citation:   Bala, A. and Ismail, I. and Ibrahim, R. and Sait, S.M. and Salami, H.O.  (2019) Prediction Using Cuckoo Search Optimized Echo State Network.  Arabian Journal for Science and Engineering, 44 (11).  pp. 9769-9778.  ISSN 2193567X