Improved algorithm on rule-based reasoning systems modeled by fuzzy Petri nets

2003 ◽  
Author(s):  
Rong Yang ◽  
Wing Shan Leung ◽  
Pheng Ann Heng ◽  
Kwong Sak Leung
Keyword(s):  
Petri Nets ◽  
Rule Based ◽  
Reasoning Systems ◽  
Fuzzy Petri Nets ◽  
Improved Algorithm

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.

scholarly journals Approximate reasoning with fuzzy rule interpolation: background and recent advances

2021 ◽  
Author(s):  
Fangyi Li ◽  
Changjing Shang ◽  
Ying Li ◽  
Jing Yang ◽  
Qiang Shen
Keyword(s):  
Fuzzy Inference ◽  
Fuzzy Rule ◽  
Approximate Reasoning ◽  
Rule Based ◽  
Reasoning Systems ◽  
Significant Interest ◽  
Recent Developments ◽  
Rule Bases ◽  
The Given ◽  
Computing Approach

AbstractApproximate reasoning systems facilitate fuzzy inference through activating fuzzy if–then rules in which attribute values are imprecisely described. Fuzzy rule interpolation (FRI) supports such reasoning with sparse rule bases where certain observations may not match any existing fuzzy rules, through manipulation of rules that bear similarity with an unmatched observation. This differs from classical rule-based inference that requires direct pattern matching between observations and the given rules. FRI techniques have been continuously investigated for decades, resulting in various types of approach. Traditionally, it is typically assumed that all antecedent attributes in the rules are of equal significance in deriving the consequents. Recent studies have shown significant interest in developing enhanced FRI mechanisms where the rule antecedent attributes are associated with relative weights, signifying their different importance levels in influencing the generation of the conclusion, thereby improving the interpolation performance. This survey presents a systematic review of both traditional and recently developed FRI methodologies, categorised accordingly into two major groups: FRI with non-weighted rules and FRI with weighted rules. It introduces, and analyses, a range of commonly used representatives chosen from each of the two categories, offering a comprehensive tutorial for this important soft computing approach to rule-based inference. A comparative analysis of different FRI techniques is provided both within each category and between the two, highlighting the main strengths and limitations while applying such FRI mechanisms to different problems. Furthermore, commonly adopted criteria for FRI algorithm evaluation are outlined, and recent developments on weighted FRI methods are presented in a unified pseudo-code form, easing their understanding and facilitating their comparisons.

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