On a class of fuzzy classification maximum likelihood procedures
Fuzzy Sets and Systems
On parameter estimation for normal mixtures based on fuzzy clustering algorithms
Fuzzy Sets and Systems
Fuzzy set theory—and its applications (3rd ed.)
Fuzzy set theory—and its applications (3rd ed.)
General C-Means Clustering Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
A systematic approach for product families formation in Reconfigurable Manufacturing Systems
Robotics and Computer-Integrated Manufacturing
Clustering: A neural network approach
Neural Networks
Partitive clustering (K-means family)
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
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Hopfield neural networks are well known forcluster analysis with an unsupervised learning scheme.This class of networks is a set of heuristicprocedures that suffers from several problems such as not guaranteedconvergence and outputdepending on the sequence of input data. In thispaper, a Compensated Fuzzy Hopfield Neural Network(CFHNN) is proposed which integrates a Compensated Fuzzy C-Means(CFCM) model into the learning scheme and updatingstrategies of the Hopfield neural network.The CFCM, modified from Penalized Fuzzy C-Meansalgorithm (PFCM), is embedded into a Hopfield net toavoid the NP-hard problem and to speed up theconvergence rate for the clustering procedure. Theproposed network also avoids determining values forthe weighting factors in the energy function. Inaddition, its training scheme enables the network tolearn more rapidly and more effectively than FCM andPFCM. In experimental results, the CFHNN method showspromising results in comparison with FCM and PFCMmethods.