Paper in Neuroprose Archive

[Paolo Frasconi]

The following technical report has been placed in the Neuroprose
Archives at Ohio State University:
 
	   Injecting Nondeterministic Finite State Automata
		    into Recurrent Neural Networks

	    Paolo Frasconi, Marco Gori, and Giovanni Soda
				   
	      Technical Report DSI-RT15/92, August 1992
		Dipartimento di Sistemi e Informatica
			University of Florence
 

Abstract:

In this paper we propose a method for injecting time-warping
nondeterministic finite state automata into recurrent neural networks.
The proposed algorithm takes as input a set of automata transition
rules and produces a recurrent architecture.  The resulting connection
weights are specified by means of linear constraints.  In this way,
the network is guaranteed to carry out the assigned automata rules,
provided the weights belong to the constrained domain and the inputs
belong to an appropriate range of values, making possible a boolean
interpretation.  In a subsequent phase, the weights can be adapted in
order to obtain the desired behavior on corrupted inputs, using
learning from examples.  One of the main concerns of the proposed
neural model is that it is no longer focussed exclusively on learning,
but also on the identification of significant architectural and weight
constraints derived systematically from automata rules, representing
the partial domain knowledge on a given problem.



To obtain a copy via FTP (courtesy of Jordan Pollack):
 
unix% ftp archive.cis.ohio-state.edu (or 128.146.8.52)
Name: anonymous
Password: (type your E-mail address)
ftp> cd pub/neuroprose
ftp> binary
ftp> get frasconi.nfa.ps.Z
ftp> quit
unix% zcat frasconi.nfa.ps.Z | lpr
                  (or however you uncompress and print postscript)
 
 
Sorry, no hard copies available.

Paolo Frasconi
Dipartimento di Sistemi e Informatica
Via di Santa Marta, 3
50139 Firenze, Italy
frasconi at ingfi1.cineca.it