A Lattice-Computing ensemble for reasoning based on formal fusion of disparate data types, and an industrial dispensing application
By "fusion" this work means integration of disparate types of data including (intervals of) real numbers as well as possibility/probability distributions defined over the totally-ordered lattice (R,   [0,1] result in the Cartesian product lattice (F-N, <=) towards decision-making based on reasoning. In conclusion, the space (F-N, <=) emerges as a formal framework for the development of hybrid intelligent fusion systems/schemes. A fuzzy lattice reasoning (FLR) ensemble scheme, namely FLR pairwise ensemble, or FLRpe for short, is introduced here for sound decision-making based on descriptive knowledge (rules). Advantages include the sensible employment of a sparse rule base, employment of granular input data (to cope with imprecision/uncertainty/vagueness), and employment of all-order data statistics. The advantages as well as the performance of our proposed techniques are demonstrated, comparatively, by computer simulation experiments regarding an industrial dispensing application. (C) 2011 Elsevier B.V. All rights reserved.
lattice-computing fuzzy lattice reasoning (flr) sparse rules disparate data fusion ensemble of experts granular data inclusion measure intervals' number (in) lattice theory