Title:
Studies-on-Fuzzy-C-Means-Based-on-Ant-Colony-Algo
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Description: A fault identification with fuzzy C-Mean clustering
algorithm based on improved ant colony algorithm (ACA) is
presented to avoid local optimization in iterative process of
fuzzy C-Mean (FCM) clustering algorithm and the difficulty in
fault classification. In the algorithm, the problem of fault
identification is translated to a constrained optimized
clustering problem. Using heuristic search of colony can find
good solutions. And according to the information content of
cluster center, it could merger surrounding data together to
cause clustering identification. The algorithm may identify
fuzzy clustering numbers and initial clustering center. It can
also prevent data classification from causing some errors.
Thus, applying in fault diagnosis shows validity of computing
and credibility of identification results.
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Studies on Fuzzy C-Means Based on Ant Colony Algorithm.pdf
