Clustering is a fundamental data analysis method. It is widely used for pattern recognition,
feature extraction, vector quantization (VQ), image segmentation, function approximation …

Conventional model-based data processing methods are computationally expensive and
require experts' knowledge for the modelling of a system. Neural networks are a model-free …

This paper presents a systematic approach to design first order Tagaki–Sugeno–Kang (TSK)
fuzzy systems. This approach attempts to obtain the fuzzy rules without any assumption …

In this study, we propose a cluster-oriented development of fuzzy models. An overall design
process is focused on an efficient usage of fuzzy clustering, Fuzzy C-Means (FCM), in …

Fuzzy models are regarded as numeric constructs and as such are optimized and evaluated
at the numeric level. In this study, we depart from this commonly accepted position and …

The COVID-19 pandemic has imposed significant challenges on global health, emphasizing
the persistent threat of large-scale infectious diseases in the future. This study addresses the …

Abstract Based on the Xie–Beni index and an improved particle swarm optimization
algorithm, a novel identification method for the Takagi–Sugeno fuzzy model is proposed in …

Given the rapidly growing diversity of techniques and applications of fuzzy clustering, an
interpretation of grouping results becomes of paramount relevance. Fuzzy clusters offer a lot …

An effective approach is developed to establish affine Takagi-Sugeno (TS) fuzzy model for a
given nonlinear system from its input–output data. Firstly, the fuzzy c-regression model …

Interpretability is the dominant feature of a fuzzy model in security-oriented fields.
Traditionally fuzzy models based on expert knowledge have obtained well interpretation …