title: Stable encoding of finite-state machines in discrete-time recurrent neural nets with sigmoid units.
abstract: There has been a lot of interest in the use of discrete-time recurrent neural nets (DTRNN) to learn finite-state tasks, with interesting results regarding the induction of simple finite-state machines from input-output strings. Parallel work has studied the computational power of DTRNN in connection with finite-state computation. This article describes a simple strategy to devise stable encodings of finite-state machines in computationally capable discrete-time recurrent neural architectures with sigmoid units and gives a detailed presentation on how this strategy may be applied to encode a general class of finite-state machines in a variety of commonly used first- and second-order recurrent neural networks. Unlike previous work that either imposed some restrictions to state values or used a detailed analysis based on fixed-point attractors, our approach applies to any positive, bounded, strictly growing, continuous activation function and uses simple bounding criteria based on a study of the conditions under which a proposed encoding scheme guarantees that the DTRNN is actually behaving as a finite-state machine.
authors.affiliation: Departament de Llenguatges i Sistemes Informàtics, Universitat d'Alacant, E-03071 Alacant, Spain.
authors.forename: R C
authors.initials: RC
authors.lastname: Carrasco
authors.forename: M L
authors.initials: ML
authors.lastname: Forcada
authors.forename: M A
authors.initials: MA
authors.lastname: Valdés-Muñoz
authors.forename: R P
authors.initials: RP
authors.lastname: Neco
mesh_terms: D008959:Models, Neurological; D015233:Models, Statistical; D009415:Nerve Net