Paper Info
Title
An Introduction To Parameterized Ifam Models With Applications In Prediction
Abstract
Fuzzy associative memories (FAMs) and, in particular, the class of implicative fuzzy associative memories (IFAMs) can be used to implement fuzzy rule-based systems. In this way, a variety of applications can be dealt with. Since there are infinitely many IFAM models, we are confronted with the problem of selecting the best IFAM model for a given application. In this paper, we restrict ourselves to a subclass of the entire class of IFAMs, namely the subclass of IFAMs that are associated with the Yager family of parameterized t-norms. For simplicity, we speak of the class of Yager IFAMs. In this setting, we formulate the problem of choosing the best Yager IFAM for a given application as an optimization problem. Considering two problems in time series prediction from the literature, we solve this optimization problem and compare the performance of the resulting Yager IFAM with the performances of other fuzzy, neural, neuro-fuzzy, and statistical techniques.
Year
Venue
Keywords
2009
PROCEEDINGS OF THE JOINT 2009 INTERNATIONAL FUZZY SYSTEMS ASSOCIATION WORLD CONGRESS AND 2009 EUROPEAN SOCIETY OF FUZZY LOGIC AND TECHNOLOGY CONFERENCE
Fuzzy associative memory, implicative fuzzy associative memory, Yager family of parameterized t-norms, time-series prediction, hydroelectric plant, monthly streamflow prediction
Field
DocType
Citations 
Time series,Parameterized complexity,Associative property,Fuzzy logic,Artificial intelligence,Optimization problem,Mathematics,Fuzzy rule
Conference
8
PageRank 
References 
Authors
0.49
11
3
Name
Order
Citations
PageRank
Peter Sussner188059.25
Rodolfo Miyasaki280.49
Marcos Eduardo Valle322617.84