Fuzzy mathematical approach to pattern recognition
Fuzzy mathematical approach to pattern recognition
Fuzzy sets, uncertainty, and information
Fuzzy sets, uncertainty, and information
Ordering, distance and closeness of fuzzy sets
Fuzzy Sets and Systems - Special issue on fuzzy data analysis
Granular neural networks for land use classification
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Granular Neural Networks With Evolutionary Interval Learning
IEEE Transactions on Fuzzy Systems
Granular neural networks for numerical-linguistic data fusion and knowledge discovery
IEEE Transactions on Neural Networks
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We introduce a robust granular neural network (RGNN) model based on the multilayer perceptron using back-propagation algorithm for fuzzy classification of patterns. We provide a development strategy of the network mainly based upon the input vector, linguistic connection weights and target vector. While the input vector is described in terms of fuzzy granules, the target vector is defined in terms of class membership values and zeros. The connection weights among nodes of RGNN are in terms of linguistic variables, whose values are updated by adding two linguistic hedges. The updated linguistic variables are called generalized linguistic variables. The node functions of RGNN are defined in terms of linguistic arithmetic operations. We present the experimental results on several real life data sets. Our results show that the classification performance of RGNN is superior to other similar type of networks.