## Fuzzy Lattice Neurocomputing (FLN) Models (2000)

Citations: | 16 - 7 self |

### BibTeX

@MISC{Kaburlasos00fuzzylattice,

author = {Vassilis G. Kaburlasos and Vassilios Petridis},

title = {Fuzzy Lattice Neurocomputing (FLN) Models},

year = {2000}

}

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### OpenURL

### Abstract

In this work it is shown how fuzzy lattice neurocomputing (FLN) emerges as a connectionist paradigm in the framework of fuzzy lattices (FL-framework) whose advantages include the capacity to deal rigorously with: disparate types of data such as numeric and linguistic data, intervals of values, "missing" and "don't care" data. A novel notation for the FL-framework is introduced here in order to simplify mathematical expressions without losing content. Two concrete FLN models are presented, namely " FLN" for competitive clustering, and "FLN with tightest fits (FLNtf)" for supervised clustering. Learning by the FLN, is rapid as it requires a single pass through the data, whereas learning by the FLNtf , is incremental, data order independent, polynomial O(n³), and it guarantees maximization of the degree of inclusion of an input in a learned class as explained in the text. Convenient geometric interpretations are provided. The FLN is presented here as fuzzyART 's extension in the FLfr...