FUZZY REASONING USING FUZZY PETRI NETS FOR THE MONITORING AND PLANNING OF CONTACT STATES IN ROBOTIC ASSEMBLY

2006 ◽  
Vol 42 (02) ◽  
pp. 43
Author(s):  
Sheng GAO
Keyword(s):  
Petri Nets ◽  
Fuzzy Reasoning ◽  
Robotic Assembly ◽  
Fuzzy Petri Nets

FUZZY PETRI NETS FOR MODELING RULE-BASED REASONING

1998 ◽  
Vol 07 (04) ◽  
pp. 463-485 ◽  
Author(s):  
JONATHAN LEE ◽  
KEVIN F. R. LIU ◽  
WEILING CHIANG
Keyword(s):  
Petri Nets ◽  
Fuzzy Rule ◽  
Fuzzy Reasoning ◽  
Fuzzy Rules ◽  
Rule Based ◽  
Imprecise Information ◽  
Fuzzy Petri Nets ◽  
Reasoning Algorithm ◽  
Modeling Rule

In this paper, a fuzzy Petri nets for modeling fuzzy rule-based reasoning is proposed to bring together the possibilistic entailment and the fuzzy reasoning to handle uncertain and imprecise information. The three key components in our fuzzy rule-based reasoning: fuzzy propositions, truth-qualified fuzzy rules, and truth-qualified fuzzy facts, can be formulated as fuzzy places, uncertain transitions, and uncertain fuzzy tokens, respectively. Four types of uncertain transitions, inference, aggregation, duplication and aggregation-duplication transitions, are introduced to meet the mechanism of fuzzy rule-based reasoning. A reasoning algorithm based on fuzzy Petri nets is also presented to improve the efficiency of fuzzy rule-based reasoning. The reasoning algorithm is consistent with not only the rule-based reasoning but also the execution of Petri nets.

HIGH-LEVEL FUZZY PETRI NETS AS A BASIS FOR MANAGING SYMBOLIC AND NUMERICAL INFORMATION

2000 ◽  
Vol 09 (04) ◽  
pp. 569-588 ◽  
Author(s):  
KEVIN F.R. LIU ◽  
JONATHAN LEE ◽  
WEILING CHIANG
Keyword(s):  
Neural Networks ◽  
Petri Nets ◽  
Fuzzy Set ◽  
Fuzzy Variable ◽  
Fuzzy Reasoning ◽  
Fuzzy Neural Networks ◽  
Numerical Information ◽  
Fuzzy Neural ◽  
Fuzzy Petri Nets ◽  
High Level

The focus of this paper is on an attempt towards a unified formalism to manage both symbolic and numerical information based on high-level fuzzy Petri nets (HLFPN). Fuzzy functions, fuzzy reasoning, and fuzzy neural networks are integrated in HLFPN In HLFPN model, a fuzzy place carries information to describe the fuzzy variable and the fuzzy set of a fuzzy condition. An arc is labeled with a fuzzy weight to represent the strength of connection between places and transitions. A fuzzy set and a fuzzy truth-value are attached to an uncertain fuzzy token to model imprecision and uncertainty. We have identified six types of uncertain transition: calculation transitions to compute functions with uncertain fuzzy inputs; inference transitions to perform fuzzy reasoning; neuron transitions to execute computations in neural networks; duplication transitions to duplicate an uncertain fuzzy token to several tokens carrying the same fuzzy sets and fuzzy truth values; aggregation transitions to combine several uncertain fuzzy tokens with the same fuzzy variable; and aggregation-duplication transitions to amalgamate aggregation transitions and duplication transitions. To guide the computation inside the HLFPN, an algorithm is developed and an example is used to illustrate the proposed approach.

A self-navigating robot using Fuzzy Petri nets

2018 ◽  
Vol 101 ◽  
pp. 153-165 ◽  
Author(s):  
Seung-yun Kim ◽  
Yilin Yang
Keyword(s):  
Petri Nets ◽  
Fuzzy Petri Nets
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