On the Interpretation of Recurrent Neural Networks as Finite State Machines

2019 ◽  
pp. 312-323
Author(s):  
Christian Oliva ◽  
Luis F. Lago-Fernández
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Finite State Machines ◽  
State Machines ◽  
Finite State

scholarly journals An Algebraic Framework to Represent Finite State Machines in Single-Layer Recurrent Neural Networks

1995 ◽  
Vol 7 (5) ◽  
pp. 931-949 ◽  
Author(s):  
R. Alquézar ◽  
A. Sanfeliu
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Single Layer ◽  
Finite State Machines ◽  
Grammatical Inference ◽  
State Machines ◽  
Linear System Of Equations ◽  
Wide Range ◽  
Finite State ◽  
Algebraic Framework

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).

Time-delay neural networks: representation and induction of finite-state machines

1997 ◽  
Vol 8 (5) ◽  
pp. 1065-1070 ◽  
Author(s):  
D.S. Clouse ◽  
C.L. Giles ◽  
B.G. Horne ◽  
G.W. Cottrell
Keyword(s):  
Neural Networks ◽  
Time Delay ◽  
Finite State Machines ◽  
State Machines ◽  
Finite State
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