Granular Fuzzy Inference System (FIS) Design by Lattice Computing

Information granules are partially/lattice-ordered Therefore, lattice computing (LC) is proposed for dealing with them The granules here are Intervals' Numbers (INs), which can represent real numbers, intervals, fuzzy numbers, probability distributions, and logic values Based on two novel theoretical propositions introduced here, it is demonstrated how LC may enhance popular fuzzy inference system (FIS) design by the rigorous fusion of granular input data, the sensible employment of sparse rules, and the introduction of tunable nonlinearities.

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