6,097 research outputs found
A Fuzzy Petri Nets Model for Computing With Words
Motivated by Zadeh's paradigm of computing with words rather than numbers,
several formal models of computing with words have recently been proposed.
These models are based on automata and thus are not well-suited for concurrent
computing. In this paper, we incorporate the well-known model of concurrent
computing, Petri nets, together with fuzzy set theory and thereby establish a
concurrency model of computing with words--fuzzy Petri nets for computing with
words (FPNCWs). The new feature of such fuzzy Petri nets is that the labels of
transitions are some special words modeled by fuzzy sets. By employing the
methodology of fuzzy reasoning, we give a faithful extension of an FPNCW which
makes it possible for computing with more words. The language expressiveness of
the two formal models of computing with words, fuzzy automata for computing
with words and FPNCWs, is compared as well. A few small examples are provided
to illustrate the theoretical development.Comment: double columns 14 pages, 8 figure
Learning Membership Functions in a Function-Based Object Recognition System
Functionality-based recognition systems recognize objects at the category
level by reasoning about how well the objects support the expected function.
Such systems naturally associate a ``measure of goodness'' or ``membership
value'' with a recognized object. This measure of goodness is the result of
combining individual measures, or membership values, from potentially many
primitive evaluations of different properties of the object's shape. A
membership function is used to compute the membership value when evaluating a
primitive of a particular physical property of an object. In previous versions
of a recognition system known as Gruff, the membership function for each of the
primitive evaluations was hand-crafted by the system designer. In this paper,
we provide a learning component for the Gruff system, called Omlet, that
automatically learns membership functions given a set of example objects
labeled with their desired category measure. The learning algorithm is
generally applicable to any problem in which low-level membership values are
combined through an and-or tree structure to give a final overall membership
value.Comment: See http://www.jair.org/ for any accompanying file
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