摘要: Computational Intelligence (CI) consists of an evolving collection methodologies often inspired from nature (Bonissone, Chen, Goebel & Khedkar, 1999, Fogel, Pedrycz, 1998). Two popular CI include neural networks and fuzzy systems. Lately, a unification was proposed in CI, at “data level”, based on lattice theory (Kaburlasos, 2006). More specifically, it shown that several types data including vectors (fuzzy) numbers, sets, 1D/2D (real) functions, graphs/trees, (strings of) symbols, etc. are partially(lattice)-ordered. In conclusion, unified cross-fertilization for knowledge representation modeling with emphasis clustering, classification, regression applications Of particular interest practice is the totally-ordered (R,≤) real which has emerged historically conventional measurement process successive comparisons. It known gives rise to hierarchy lattices (F,≤) interval or FINs short (Papadakis Kaburlasos, 2007). This article shows extensions two networks, i.e. fuzzy-ARTMAP (Carpenter, Grossberg, Markuzon, Reynolds Rosen 1992) self-organizing map (Kohonen, 1995), as well extension inference systems (Mamdani Assilian, 1975), FINs. Advantages aforementioned both capacity rigorously deal nonnumeric input introduce tunable nonlinearities. Rule induction yet another advantage.
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