Unsupervised Optimal Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
A Robust Competitive Clustering Algorithm With Applications in Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Machine Learning
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In the structural dynamics area, system identification represents one of the most critical steps. Therefore, a great research effort has been made in the last decades to improve the accuracy and reliability of this process. One of the results of this effort is the so-called stabilization diagram, a widespread tool, where the system's poles are represented for several model orders. In real applications, the assessment of this diagram is often extremely demanding due to the high number of poles amongst which only a few represent the true or physical ones.Fuzzy clustering was already successfully applied in various fields including economics, finance and marketing. In this paper, fuzzy clustering is introduced into the structural mechanics field as a tool to automatically assess stabilization diagrams. Several advanced algorithms are presented, all based on the Fuzzy-C-Means clustering technique, including the Gustafson-Kessel and Gath-Geva algorithms. In addition, Genetic Algorithms can also be used to cluster a data set as a stand-alone technique as well as in a hybrid combination with fuzzy clustering algorithms.This paper concludes with a comparison of all the mentioned approaches, by applying them on true in-flight test data.