Fuzzy ART architecture papers online

[Matthias Blume]

Dear Connectionists,

Two papers describing a simple and efficient architecture for Fuzzy ART and 
Fuzzy ARTMAP are now available online.  (Sorry, hardcopies are not available.)

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Matthias Blume and Sadik C. Esener, 
An efficient mapping of Fuzzy ART onto a neural architecture (5 pages), 
submitted to Neural Networks.

A novel mapping of the Fuzzy ART algorithm onto a neural network architecture 
is described.  The architecture does not utilize bi-directional synapses, 
weight transport, or weight duplication, and requires one fewer layer of 
processing elements than the architecture originally proposed by Carpenter, 
Grossberg, & Rosen (1991).  In the new architecture, execution of the 
algorithm takes constant time per input vector regardless of the relationship 
between the input and existing templates, and several control signals are 
eliminated.  This mapping facilitates hardware implementation of Fuzzy ART and 
furthermore serves as a tool for envisioning and understanding the algorithm.

Keywords:  Fuzzy ART, Fuzzy ARTMAP, parallel hardware, neural architecture.

ftp://archive.cis.ohio-state.edu/pub/neuroprose/blume.fam_arch.ps.Z
http://icse1.ucsd.edu/~mablume/nnletter.ps

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Matthias Blume and Sadik C. Esener, 
Optoelectronic Fuzzy ARTMAP processor, 
Optical Computing, Vol. 10, 1995 OSA Technical Digest Series
(Optical Society of America, Washington, DC, 1995), p. 213-215, March 1995.

The Fuzzy ARTMAP algorithm can perform well even with weights truncated to 4 
bits during training.  Furthermore, only the weights corresponding to one 
processing element are updated after each training sample.  Finally, it 
converges rapidly and relatively uniformly with little dependence on the 
particular choice of adjustable parameter values and initial state.  These 
characteristics are particularly advantageous for parallel optoelectronic 
implementations.  We map Fuzzy ARTMAP onto an architecture which satisfies the 
constraints of the hardware, and suggest an implementation which is an 
appropriate combination of optical and electronic technology.  The proposed 
mapping of the algorithm onto a neural architecture is efficient, requiring 
only an input layer and one processing layer per fuzzy ART module, and 
requiring neither weight transport nor multiple copies of weights.  The 
proposed optoelectronic system is simple, yet versatile, and relies on proven 
components.

Keywords:  Parallel optoelectronic hardware, Fuzzy ART, neural architecture.

ftp://archive.cis.ohio-state.edu/pub/neuroprose/blume.oe_fam.ps.Z
http://icse1.ucsd.edu/~mablume/OSA95.ps

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- Matthias Blume
  ECE department, UCSD
  matthias at ucsd.edu
  http://icse1.ucsd.edu/~mablume