Please use this identifier to cite or link to this item: http://ktisis.cut.ac.cy/handle/10488/4024
Title: An adaptive resource allocating neural fuzzy inference system
Authors: Pertselakis, Minas 
Tsapatsoulis, Nicolas 
Kollias, Stefanos D. 
Stafylopatis, Andreas 
Keywords: Knowledge based systems
Online adaptation
Data-driven knowledge extraction
Resource allocation
Issue Date: 2003
Source: 12th IEEE Intelligent Systems Application to Power Systems, 2003, Lemnos, Greece, September
Abstract: Adaptivity to non-stationary contexts is a very important property for intelligent systems in general, as well as to a variety of applications of knowledge based systems in the area of Electric Power Systems. In this paper we present an innovative Neural-Fuzzy architecture that exhibits three important properties: online adaptation, knowledge (rule) modeling, and knowledge extraction from numerical data. The ARANFIS (Adaptive Resource Allocating Neural Fuzzy Inference System) has an adaptive structure, which is formed during the training process. We show that the resource allocating methodology enables both online adaptation and rule extraction; the latter differentiates it from the majority of Neurofuzzy systems with fixed structure which perform mainly rule modification/ adaptation rather that rule extraction. The efficiency of the system has been tested on both publicly available data, as well as on a real generated dataset of a 120 MW power plant.
URI: http://ktisis.cut.ac.cy/jspui/handle/10488/4024
Appears in Collections:Δημοσιεύσεις σε συνέδρια/Conference papers

Files in This Item:
File Description SizeFormat 
Tsapatsoulis.pdf569.5 kBAdobe PDFView/Open
Show full item record

Page view(s)

4
checked on Mar 27, 2017

Download(s) 20

2
checked on Mar 27, 2017

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.