Filtration of Non-Monotonic Rules for Fuzzy Rule Base Compression

DOI

10.1080/18756891.2013.858904How to use a DOI?

Keywords

Fuzzy systems, Complexity theory, Simulation, Data compression

Abstract

This paper proposes a rule base compression method for fuzzy systems. The method is based on filtration of rules with identical linguistic values for the output that are known as non-monotonic rules. The filtration removes the redundant computations in the fuzzy inference with respect to the crisp values of the inputs to the fuzzy system. The method identifies the redundant rules after fuzzification and removes them while preserving the defuzzified output from the fuzzy system for each simulation cycle. In comparison to the known rule base reduction methods, this rule base compression method does not compromise the solution and has better efficiency in terms of on-line computations. The method processes the rule base for a fuzzy system during simulation cycles by contracting it to a rule base of a smaller size at the start of each inference stage and then expanding it to its original size before the next fuzzification stage.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Alexander Gegov
AU  - Neelamugilan Gobalakrishnan
AU  - David Sanders
PY  - 2014
DA  - 2014/04/01
TI  - Filtration of Non-Monotonic Rules for Fuzzy Rule Base Compression
JO  - International Journal of Computational Intelligence Systems
SP  - 382
EP  - 400
VL  - 7
IS  - 2
SN  - 1875-6883
UR  - https://doi.org/10.1080/18756891.2013.858904
DO  - 10.1080/18756891.2013.858904
ID  - Gegov2014
ER  -