Learning and Tuning of Fuzzy Rules

In this paper, we will propose a two-context fuzzy clustering algorithm (2C-FCM) and its parallel solution so called P2C-FCM for the classification problems. Some initial experiments show the effectiveness of P2C-FCM and 2C-FCM when comparing with traditional Context FCM. The applications of P2C-FCM and 2C-FCM are the basis to generate fuzzy rules for classifying member countries of United Nation Organization (UNO) according to the Human Development Index based on the statistics of UNO in 2005.

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A new approach for tuning control rule based on hedge algebras theory and application in structural vibration control

Bui

Tran

2020

Journal of Vibration and Control

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The hedge algebras theory has the potential to make significant applications in the field of computational intelligence. The purpose of the present study is to improve the control performance of the hedge algebras–based controller by tuning its control rules and apply the hedge algebras–based controller using the tuned rule base in vibration control of structures. The authors propose a “tuning coefficient” to express the impact of each rule of the controller in the control process. These control rules are adjusted by optimizing the above tuning coefficient. The tuned controller is then used to reduce the dynamic response of structures subjected to different excitations. The adjusted rule base is more appropriate for the model to be controlled, and it allows enhancing the control performance of the system. The proposed approach is uncomplicated and transparent, and it allows preserving the monotonous feature of the rule base.

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Learning and Tuning of Fuzzy Rules

Cited by 3 publications

References 19 publications

Learning Fuzzy Decision Rules

Learning Fuzzy Decision Rules

Some context fuzzy clustering methods for classification problems

A new approach for tuning control rule based on hedge algebras theory and application in structural vibration control

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