Learning and Extracting Finite State Automata with Second-Order Recurrent Neural Networks

@article{Giles1992LearningAE,
  title={Learning and Extracting Finite State Automata with Second-Order Recurrent Neural Networks},
  author={C. Lee Giles and Clifford B. Miller and Dong Chen and Hsing-Hen Chen and Guo-Zheng Sun and Yee-Chun Lee},
  journal={Neural Computation},
  year={1992},
  volume={4},
  pages={393-405}
}
We show that a recurrent, second-order neural network using a real-time, forward training algorithm readily learns to infer small regular grammars from positive and negative string training samples. We present simulations that show the effect of initial conditions, training set size and order, and neural network architecture. All simulations were performed with random initial weight strengths and usually converge after approximately a hundred epochs of training. We discuss a quantization… 
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