Download the full-sized PDF of Neuro-fuzzy architectures based on complex fuzzy logicDownload the full-sized PDF



Permanent link (DOI):


Export to: EndNote  |  Zotero  |  Mendeley


This file is in the following communities:

Graduate Studies and Research, Faculty of


This file is in the following collections:

Theses and Dissertations

Neuro-fuzzy architectures based on complex fuzzy logic Open Access


Other title
fuzzy logic, complex fuzzy logic, time series dataset
Type of item
Degree grantor
University of Alberta
Author or creator
Sara, Aghakhani
Supervisor and department
Dick, Scott (Electrical and Computer Engineering)
Examining committee member and department
Lu, Paul (Computing Science)
Musilek, Petr (Electrical and Computer Engineering)
Electrical and Computer Engineering

Date accepted
Graduation date
Master of Science
Degree level
Complex fuzzy logic is a new type of multi-valued logic, in which truth values are drawn from the unit disc of the complex plane; it is thus a generalization of the familiar infinite-valued fuzzy logic. At the present time, all published research on complex fuzzy logic is theoretical in nature, with no practical applications demonstrated. The utility of complex fuzzy logic is thus still very debatable. In this thesis, the performance of ANCFIS is evaluated. ANCFIS is the first machine learning architecture to fully implement the ideas of complex fuzzy logic, and was designed to solve the important machine-learning problem of time-series forecasting. We then explore extensions to the ANCFIS architecture. The basic ANCFIS system uses batch (offline) learning, and was restricted to univariate time series prediction. We have developed both an online version of the univariate ANCFIS system, and a multivariate extension to the batch ANCFIS system.
License granted by Sara Aghakhani ( on 2010-01-05T17:15:54Z (GMT): Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of the above terms. The author reserves all other publication and other rights in association with the copyright in the thesis, and except as herein provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
Citation for previous publication

File Details

Date Uploaded
Date Modified
Audit Status
Audits have not yet been run on this file.
File format: pdf (Portable Document Format)
Mime type: application/pdf
File size: 2450451
Last modified: 2015:10:12 13:07:06-06:00
Filename: Aghakhani_Sara_Spring 2010.pdf
Original checksum: dc9fa591cb5a425f743911de0bd451ed
Well formed: true
Valid: true
File title: University of Alberta-1
File author: Sara
Page count: 134
Activity of users you follow
User Activity Date