NDLI: Synaptic and somatic operators for fuzzy neurons: which t-norms to choose?

Content Provider	IEEE Xplore Digital Library
Author	Stoica, A.
Copyright Year	1996
Description	Author affiliation: Dept. of Electr. & Electron. Eng., Victoria Univ. of Technol., Melbourne, Vic., Australia (Stoica, A.)
Abstract	Fuzzy (logic) neurons have triangular norms as synaptic operators and triangular s-norms as somatic operators. MIN and MAX are often the chosen t-norm/s-norm pair. This choice is mainly due to the simplicity of the calculations and to the fact that a layer of MAX-MIN neurons implements the widely-used MAX-MIN composition. From a neural networks perspective (i.e. having in mind the ability to use the learning and adaptation mechanisms used with classic neuron models), other t-norms may be more suitable for defining fuzzy neurons. A set of three conditions was chosen to reflect the suitability of t-norms for implementing synaptic and somatic operators. The conditions reflect the need to allow gradient-descent learning, parametric adaptation and the ability to reduce to the MAX-MIN model as a particular case. A set of 12 t-norm pairs was analyzed to asses how well they match the chosen criteria. Only two t-norm pairs satisfy all the criteria, one of them (the fundamental t-norm pair, also known as Frank's t-norms) having the advantage of covering the product-probabilistic sum case. The fundamental t-norms are chosen to define the fundamental fuzzy neuron.
Starting Page	55
Ending Page	58
File Size	382157
Page Count	4
File Format	PDF
ISBN	0780332253
DOI	10.1109/NAFIPS.1996.534703
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1996-06-19
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neurons Equations Topology Hardware Genetic algorithms Fuzzy logic
Content Type	Text
Resource Type	Article

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