Fuzzy weighted averages and implementation of the extension principle
Fuzzy Sets and Systems
A neural network architecture for classification of fuzzy inputs
Fuzzy Sets and Systems
A learning algorithm of fuzzy neural networks with triangular fuzzy weights
Fuzzy Sets and Systems - Special issue on fuzzy neural control
Fuzzy sets and fuzzy logic: theory and applications
Fuzzy sets and fuzzy logic: theory and applications
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Computation over Fuzzy Quantities
Computation over Fuzzy Quantities
Fuzzy perceptron neural networks for classifiers with numerical data and linguistic rules as inputs
IEEE Transactions on Fuzzy Systems
Analysis and efficient implementation of a linguistic fuzzy c-means
IEEE Transactions on Fuzzy Systems
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For many years, one of the problems in pattern recognition is classification. There are many methods proposed to deal with this type of problem. The data sets are sometimes in the binary form (real number) and represented by vectors of binary numbers (real numbers) although there are uncertainties in the data. This study is concerned with a linguistic perceptron with vectors of fuzzy numbers as inputs. This algorithm is based on the extension principle and the decomposition theorem. A synthetic data set has been utilized to illustrate the behavior of this linguistic version of perceptron. We compare the result from the linguistic perceptron with that from the regular perceptron.