Fuzzy Rule Bases Automated Design with Self-configuring Evolutionary Algorithm

2014 ◽  
Author(s):  
Eugene Semenkin ◽  
Vladimir Stanovov
Keyword(s):  
Evolutionary Algorithm ◽  
Fuzzy Rule ◽  
Automated Design ◽  
Rule Bases

A multi-objective evolutionary algorithm for the tuning of fuzzy rule bases for uncoordinated intersections in autonomous driving

2015 ◽  
Vol 321 ◽  
pp. 14-30 ◽  
Author(s):  
E. Onieva ◽  
U. Hernandez-Jayo ◽  
E. Osaba ◽  
A. Perallos ◽  
X. Zhang
Keyword(s):  
Evolutionary Algorithm ◽  
Fuzzy Rule ◽  
Autonomous Driving ◽  
Multi Objective ◽  
Rule Bases

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.

Reasoning conditions on Kóczy's interpolative reasoning method in sparse fuzzy rule bases

1995 ◽  
Vol 75 (1) ◽  
pp. 63-71 ◽  
Author(s):  
Shi Yan ◽  
Masaharu Mizumoto ◽  
Wu Zhi Qiao
Keyword(s):  
Fuzzy Rule ◽  
Rule Bases

scholarly journals Neuro-Fuzzy Assessment of Building Damage and Safety After an Earthquake

2007 ◽  
pp. 123-157 ◽  
Author(s):  
Martha Carreño ◽  
Omar Cardona ◽  
Alex Barbat
Keyword(s):  
Decision Making ◽  
Fuzzy Sets ◽  
Fuzzy System ◽  
Fuzzy Rule ◽  
Neuro Fuzzy ◽  
Computational Intelligence Technique ◽  
Artificial Neural ◽  
Fuzzy Assessment ◽  
Rule Bases ◽  
Building Occupancy

This chapter describes the algorithmic basis of a computational intelligence technique, based on a neuro-fuzzy system, developed with the objective of assisting nonexpert professionals of building construction to evaluate the damage and safety of buildings after strong earthquakes, facilitating decision-making during the emergency response phase on their habitability and reparability. A hybrid neuro-fuzzy system is proposed, based on a special three-layer feedforward artificial neural network and fuzzy rule bases. The inputs to the system are fuzzy sets, taking into account that the damage levels of the structural components are linguistic variables, defined by means of qualifications such as slight, moderate or severe, which are very appropriate to handle subjective and incomplete information. The chapter is a contribution to the understanding of how soft computing applications, such as artificial neural networks and fuzzy sets, can be used to complex and urgent processes of engineering decision-making, like the building occupancy after a seismic disaster.

A Fuzzy Approach to Disaster Modeling

2013 ◽  
pp. 1038-1055
Author(s):  
Jan Stoklasa
Keyword(s):  
Disaster Response ◽  
Initial Phase ◽  
Input Data ◽  
Fuzzy Model ◽  
Fuzzy Rule ◽  
Linguistic Variables ◽  
Rescue Services ◽  
Uncertain Input ◽  
Uncertain Input Data ◽  
Rule Bases

The decision making process of the Emergency Medical Rescue Services (EMRS) operations centre during disasters involves a significant amount of uncertainty. Decisions need to be made quickly, and no mistakes are tolerable, particularly in the case of disasters resulting in a large number of injured people. A multiphase linguistic fuzzy model is introduced to assist the operator during the initial phase of the medical disaster response. Based on uncertain input data, estimating the severity of the disaster, the number of injured people, and the amount of forces and resources needed to successfully deal with the situation is possible. The need for reinforcements is also considered. Fuzzy numbers, linguistic variables and fuzzy rule bases are applied to deal with the uncertainty. Outputs provided by the model (severity of the disaster, number of reinforcements needed etc.) are available both as fuzzy sets (for the purposes of disaster planning) and linguistic terms (for emergency call evaluation purposes).

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