Granular computing in neural networks
Granular computing
Communicating between information systems
Information Sciences: an International Journal
A weighted fuzzy c-means clustering model for fuzzy data
Computational Statistics & Data Analysis
Fault diagnosis in railway track circuits using Dempster-Shafer classifier fusion
Engineering Applications of Artificial Intelligence
Information Sciences: an International Journal
Communication between information systems with covering based rough sets
Information Sciences: an International Journal
A Fuzzy Clustering Model for Fuzzy Data with Outliers
International Journal of Fuzzy System Applications
A granular neural network: Performance analysis and application to re-granulation
International Journal of Approximate Reasoning
Rule base simplification by using a similarity measure of fuzzy sets
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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Two models are discussed that integrate heterogeneous fuzzy data of three types: real numbers, real intervals, and real fuzzy sets. The architecture comprises three modules: 1) an encoder that converts the mixed data into a uniform internal representation; 2) a numerical processing core that uses the internal representation to solve a specified task; and 3) a decoder that transforms the internal representation back to an interpretable output format. The core used in this study is fuzzy clustering, but there are many other operations that are facilitated by the models. Two schemes for encoding the data and decoding it after clustering are presented. One method uses possibility and necessity measures for encoding and several variants of a center of gravity defuzzification method for decoding. The second approach uses piecewise linear splines to encode the data and decode the clustering results. Both procedures are illustrated using two small sets of heterogeneous fuzzy data