K. Uehara et K. Hirota, PARALLEL AND MULTISTAGE FUZZY INFERENCE BASED ON FAMILIES OF ALPHA-LEVEL SETS, Information sciences, 106(1-2), 1998, pp. 159-195
Citations number
37
Categorie Soggetti
Computer Science Information Systems","Computer Science Information Systems
In this paper, methods for parallel fuzzy inference and multistage-par
allel fuzzy inference are studied on the basis of families of alpha-le
vel sets. The parallel fuzzy inference is characterized by the unifica
tion of inference consequences obtained from a number of conditional p
ropositions. Thus, in this paper, the methods for the unification of i
nference consequences via alpha-level sets are presented first. It is
found that the unification approximated by using fuzzy convex hull is
efficient in the case where the unification is performed by the maximu
m operation. The methods for defuzzification are also examined via alp
ha-level sets for the unified consequences. The computational efficien
cy is evaluated in order to show the effectiveness of the unification
and defuzzification via alpha-level sets. Moreover, it is studied by c
omputer simulations how the approximation by fuzzy convex hull affects
the performance in fuzzy control. The results indicate that this appr
oximation does not degrade the control performance. Next, the multista
ge-parallel fuzzy inference is considered from the operational point o
f view via alpha-level sets, The multistage-parallel fuzzy inference i
s characterized by passing the unified consequence of parallel fuzzy i
nference in each stage to the next stage as a fact. Hence, the studies
are focused on this consequence passing in this paper. It is larified
that the straightforward way of inference operations via alpha-level
sets is time consuming because of the nonconvexity in the unified infe
rence consequence in each stage. In order to solve the problem, the mu
ltistage-parallel fuzzy inference is formulated into a form of linguis
tic-truth-value propagation. As a result, the inference operations in
middle stages can be conducted by convex fuzzy sets and then efficient
computations for inference is provided. The computational efficiency
is also evaluated to show the effectiveness of the formulation. Finall
y, this paper concludes with some brief discussions. (C) 1998 Elsevier
Science Inc. All rights reserved.