By Topic

Training fuzzy number neural networks with alpha-cut refinements

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
J. Dunyak ; Dept. of Math., Texas Tech. Univ., Lubbock, TX, USA ; D. Wunsch

In a fuzzy number neural network, the inputs, weights, and outputs are general fuzzy numbers. The requirement that F¯α(1) ⊂F¯α(2) whenever α(1)>α(2) imposes an enormous number of constraints on the weight parameterizations during training. This problem can be solved through a careful choice of weight representation. This new representation is unconstrained, so that standard neural network training techniques may be applied. Unfortunately, fuzzy number neural networks still have many parameters to pick during training, since each weight is represented by a vector. Thus moderate to large fuzzy number neural networks suffer from the usual maladies of very large neural networks. In this paper, we discuss a method for effectively reducing the dimensionality of networks during training. Each fuzzy number weight is represented by the endpoints of its α-cuts for some discretization 0⩽α12<...<αn ⩽1. To reduce dimensionality, training is first done using only a small subset of the αi. After successful training, linear interpolation is used to estimate additional α-cut endpoints. The network is then retrained to tune these interpolated values. This refinement is repeated as needed until the network is fully trained at the desired discretization in α

Published in:

Systems, Man, and Cybernetics, 1997. Computational Cybernetics and Simulation., 1997 IEEE International Conference on  (Volume:1 )

Date of Conference:

12-15 Oct 1997