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Open Access item An algebraic framework to represent finite state machines in single-layer recurrent neural networks

Authors:Alquézar, René
Sanfeliu Cortés, Alberto
Keywords:Pattern recognition
Issue Date:1995
Publisher:M.I.T. Press
Citation:Neural Computation 7(5): 931-949 (1995)
Abstract:In this paper we present an algebraic framework to represent finite state machines (FSMs) in single-layer recurrent neural networks (SLRNNs), which unifies and generalizes some of the previous proposals. This framework is based on the formulation of both the state transition function and the output function of an FSM as a linear system of equations, and it permits an analytical explanation of the representational capabilities of first-order and higher-order SLRNNs. The framework can be used to insert symbolic knowledge in RNNs prior to learning from examples and to keep this knowledge while training the network. This approach is valid for a wide range of activation functions, whenever some stability conditions are met. The framework has already been used in practice in a hybrid method for grammatical inference reported elsewhere (Sanfeliu and Alquézar 1994).
Publisher version (URL):http://dx.doi.org/10.1162/neco.1995.7.5.931
URI:http://hdl.handle.net/10261/30551
ISSN:0899-7667
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Appears in Collections:(IRII) Artículos

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