Paper available "Connectionist Model Based on an Optical Thin-Film Model"

[Xiaodong Li, Otago University, New Zealand]

FTP-host: archive.cis.ohio-state.edu
FTP-filename:/pub/neuroprose/xli.thinfilm.ps.Z

The file xli.thinfilm.ps.Z is now available for ftp from Neuroprose repository.


	Connectionist Learning Using an Optical Thin-Film Model (4 pages)

			Martin Purvis and Xiaodong Li
			Computer and Information Science 
			University of Otago
			Dunedin, New Zealand

ABSTRACT:

An alternative connectionist architecture to the one based on the neuroanatomy 
of biological organisms is described.  The proposed architecture is based on 
an optical thin-film multilayer model, with the thicknesses of thin-film layers
serving as adjustable 'weights' for the computation.  Inputs are encoded into 
the corresponding refractive indices of individual thin-film layers, while the 
outputs are typically measured by the overall reflection coefficients off the 
thin-film layers, at different wavelengths.  The nature of the model and some 
example calculations (a pattern recognition and the classification on the iris 
data set) that exhibit behaviour typical of conventional connectionist 
architectures are described.  This model has also been used in solving the XOR 
and 16 four-bit parity problems, and it has demonstrated comparable performance
to that of a conventional feed-forward neural netwrok model using 
Back-propagation learning. 

This paper is also available at the proceeding of the Second New Zealand 
International Two-Stream Conference on Artificial Neural Nteworks and Expert 
Systems (ANNES'95), IEEE Computer Society Press, Los Almamitos, California, 
1995, pp. 63-66.

Comments are greatly appreciated.


-- Xiaodong Li 
Email: Xiaodong at otago.ac.nz
Http: http://divcom.otago.ac.nz:800/COM/INFOSCI/SECML/xdli/xiao.htm
(Postscript file of this paper is also available here at my hoempage)