A fuzzy clustering-based rapid prototyping for fuzzy rule-based modeling

A fuzzy clustering-based rapid prototyping for fuzzy rule-based modeling

This paper presents different approaches to the problem of fuzzy rules extraction by using fuzzy clustering as the main tool. Within these approaches we describe six methods that represent different alternatives in the fuzzy modeling process and how they can be integrated with a genetic algorithms. These approaches attempt to obtain a first approximation to the fuzzy rules without any assumptio...

Entropy-based fuzzy clustering and fuzzy modeling

Fuzzy clustering is capable of finding vague boundaries that crisp clustering fails to obtain. But time complexity of fuzzy clustering is usually high, and the need to specify complicated parameters hinders its use. In this paper, an entropy-based fuzzy clustering method is proposed. It automatically identifies the number and initial locations of cluster centers. It calculates the entropy at ea...

A Clustering-Based Method for Fuzzy Modeling

In this paper, a clustering-based method is proposed for automatically constructing a multi-input TakagiSugeno (TS) fuzzy model where only the input-output data of the identified system are available. The TS fuzzy model is automatically generated by the process of structure identification and parameter identification. In the structure identification step, a clustering method is proposed to prov...

Fuzzy Rule Based Modeling as a Universalapproximation

A New Clustering-based Approach for Modeling Fuzzy Rule-based Classification Systems

In the present study, we propose a novel clustering-based method for modeling accurate fuzzy rule-based classification systems. The new method is a combination of a data mapping method, subtractive clustering method and an efficient gradient descent algorithm. A data mapping method considers the intricate geometric relationships that may exist among the data and computes a new representation of...