Fuzzy lattice neural network (FLNN): a hybrid model for learning

— This paper proposes two hierarchical schemes for learning, one for clustering and the other for classification problems. Both schemes can be implemented on a fuzzy lattice neural network (FLNN) architecture, to be introduced herein. The corresponding two learning models draw on adaptive resonance theory (ART) and min–max neurocomputing principles but their application domain is a mathematical lattice. Therefore they can handle more general types of data in addition to NNN-dimensional vectors. The FLNN neural model stems from a cross-fertilization of lattice theory and fuzzy set theory. Hence a novel theoretical foundation is introduced in this paper, that is the framework of fuzzy lattices or FL-framework, based on the concepts fuzzy lattice and inclusion measure. Sufficient conditions for the existence of an inclusion measure in a mathematical lattice are shown. The performance of the two FLNN schemes, that is for clustering and for classification, compares quite well with ot...

• Type: Journal
• Year: 1998
• Where: TNN
• Authors: Vassilios Petridis, Vassilis G. Kaburlasos
• Keywords: Fuzzy Lattices, Lattice Neural Network, Mathematical Lattice, TNN 1998