eprintid: 10926
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/09/26
datestamp: 2023-11-09 16:37:32
lastmod: 2023-11-09 16:37:32
status_changed: 2023-11-09 16:32:31
type: article
metadata_visibility: show
creators_name: Lemma, T.A.
title: Model Identification Using Neuro-Fuzzy Approach
ispublished: pub
note: cited By 1
abstract: This chapter contains the discussion on fundamental concepts related to nonlinear model identification. First, linear in parameter model identification techniques are presented. This covers static and dynamic systems. Following that, the idea of developing nonlinear models in the framework of Orhonormal Basis Functions (OBF) is described. In Sect. 3.3, basic theory of neural networks and fuzzy systems are elaborated. In the state of the art designs, one of them is constructed in the structure of the other allowing the development of a transparent model that can be trained with relatively minimal effort. Section 3.4 is dedicated to the discussion of nonlinear system identification using combined version of neural networks and fuzzy systems. Last section of the chapter deals with three different model training algorithms Least squares based, back-propagation and particle swarm optimization. © 2018, Springer International Publishing AG.
date: 2018
publisher: Springer Verlag
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85039982132&doi=10.1007%2f978-3-319-71871-2_3&partnerID=40&md5=5b2e4f17de24ac277fb456d437b527ce
id_number: 10.1007/978-3-319-71871-2₃
full_text_status: none
publication: Studies in Computational Intelligence
volume: 743
pagerange: 37-74
refereed: TRUE
issn: 1860949X
citation:   Lemma, T.A.  (2018) Model Identification Using Neuro-Fuzzy Approach.  Studies in Computational Intelligence, 743.  pp. 37-74.  ISSN 1860949X