Multi-objective evolutionary learning of granularity, membership function parameters and rules of Mamdani fuzzy systems

2009 ◽  
Vol 2 (1-2) ◽  
pp. 21-37 ◽  
Author(s):  
Michela Antonelli ◽  
Pietro Ducange ◽  
Beatrice Lazzerini ◽  
Francesco Marcelloni
Keyword(s):  
Membership Function ◽  
Fuzzy Systems ◽  
Evolutionary Learning ◽  
Multi Objective

A Review of Multi-Objective Evolutionary Based Fuzzy Classifiers

2020 ◽  
Vol 13 (1) ◽  
pp. 77-85
Author(s):  
Praveen Kumar Dwivedi ◽  
Surya Prakash Tripathi
Keyword(s):  
Fuzzy Systems ◽  
Evolutionary Process ◽  
Fuzzy Rule ◽  
Fuzzy Classification ◽  
Multi Objective Optimization ◽  
Fuzzy Classifier ◽  
Rule Based ◽  
Trade Off ◽  
Multi Objective ◽  
Rule Based Systems

Background: Fuzzy systems are employed in several fields like data processing, regression, pattern recognition, classification and management as a result of their characteristic of handling uncertainty and explaining the feature of the advanced system while not involving a particular mathematical model. Fuzzy rule-based systems (FRBS) or fuzzy rule-based classifiers (mainly designed for classification purpose) are primarily the fuzzy systems that consist of a group of fuzzy logical rules and these FRBS are unit annexes of ancient rule-based systems, containing the "If-then" rules. During the design of any fuzzy systems, there are two main objectives, interpretability and accuracy, which are conflicting with each another, i.e., improvement in any of those two options causes the decrement in another. This condition is termed as Interpretability –Accuracy Trade-off. To handle this condition, Multi-Objective Evolutionary Algorithms (MOEA) are often applied within the design of fuzzy systems. This paper reviews the approaches to the problem of developing fuzzy systems victimization evolutionary process Multi-Objective Optimization (EMO) algorithms considering ‘Interpretability-Accuracy Trade-off, current research trends and improvement in the design of fuzzy classifier using MOEA in the future scope of authors. Methods: The state-of-the-art review has been conducted for various fuzzy classifier designs, and their optimization is reviewed in terms of multi-objective. Results: This article reviews the different Multi-Objective Optimization (EMO) algorithms in the context of Interpretability -Accuracy tradeoff during fuzzy classification. Conclusion: The evolutionary multi-objective algorithms are being deployed in the development of fuzzy systems. Improvement in the design using these algorithms include issues like higher spatiality, exponentially inhabited solution, I-A tradeoff, interpretability quantification, and describing the ability of the system of the fuzzy domain, etc. The focus of the authors in future is to find out the best evolutionary algorithm of multi-objective nature with efficiency and robustness, which will be applicable for developing the optimized fuzzy system with more accuracy and higher interpretability. More concentration will be on the creation of new metrics or parameters for the measurement of interpretability of fuzzy systems and new processes or methods of EMO for handling I-A tradeoff.

scholarly journals A New Design of Membership-Function-Dependent Controller for T-S Fuzzy Systems Under Imperfect Premise Matching

2019 ◽  
Vol 27 (7) ◽  
pp. 1428-1440 ◽  
Author(s):  
Changzhu Zhang ◽  
Hak-Keung Lam ◽  
Jianbin Qiu ◽  
Chengju Liu ◽  
Qijun Chen
Keyword(s):  
Membership Function ◽  
Fuzzy Systems ◽  
Imperfect Premise Matching

Fairer Machine Learning Through Multi-objective Evolutionary Learning

2021 ◽  
pp. 111-123
Author(s):  
Qingquan Zhang ◽  
Jialin Liu ◽  
Zeqi Zhang ◽  
Junyi Wen ◽  
Bifei Mao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Evolutionary Learning ◽  
Multi Objective

scholarly journals A NOVEL EVOLUTIONARY LEARNING TECHNIQUE FOR MULTI-OBJECTIVE ARRAY ANTENNA OPTIMIZATION

2004 ◽  
Vol 48 ◽  
pp. 125-144 ◽  
Author(s):  
Yee Hui Lee ◽  
Brian J. Cahill ◽  
Stuart J. Porter ◽  
Andrew C. Marvin
Keyword(s):  
Array Antenna ◽  
Evolutionary Learning ◽  
Multi Objective ◽  
Learning Technique

scholarly journals Learning concurrently partition granularities and rule bases of Mamdani fuzzy systems in a multi-objective evolutionary framework

2009 ◽  
Vol 50 (7) ◽  
pp. 1066-1080 ◽  
Author(s):  
Michela Antonelli ◽  
Pietro Ducange ◽  
Beatrice Lazzerini ◽  
Francesco Marcelloni
Keyword(s):  
Fuzzy Systems ◽  
Multi Objective ◽  
Rule Bases

A robust multi-objective DPDC for uncertain T–S fuzzy systems

2008 ◽  
Vol 159 (20) ◽  
pp. 2749-2762 ◽  
Author(s):  
Jafar Razavi-Panah ◽  
Vahid Johari Majd
Keyword(s):  
Fuzzy Systems ◽  
Multi Objective
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