Analysis of dynamical recognizers

Fractals everywhere

Fractals everywhere

The Induction of Dynamical Recognizers

Machine Learning - Connectionist approaches to language learning

Random DFA's can be approximately learned from sparse uniform examples

COLT '92 Proceedings of the fifth annual workshop on Computational learning theory

On the computational power of neural nets

COLT '92 Proceedings of the fifth annual workshop on Computational learning theory

Learning and extracting finite state automata with second-order recurrent neural networks

Neural Computation

Induction of finite-state languages using second-order recurrent networks

Neural Computation

The calculi of emergence: computation, dynamics and induction

Proceedings of the NATO advanced research workshop and EGS topical workshop on Chaotic advection, tracer dynamics and turbulent dispersion

Learning finite machines with self-clustering recurrent networks

Neural Computation

Learning and extracting initial mealy automata with a modular neural network model

Neural Computation

Learning the initial state of a second-order recurrent neural network during regular-language inference

Neural Computation

Finite state machines and recurrent neural networks—automata and dynamical systems approaches

Finite state machines and recurrent neural networks—automata and dynamical systems approaches

Exploring the computational capabilities of recurrent neural networks

Exploring the computational capabilities of recurrent neural networks

Extraction of rules from discrete-time recurrent neural networks

Neural Networks

Introduction to Automata Theory, Languages, and Computation (3rd Edition)

Introduction to Automata Theory, Languages, and Computation (3rd Edition)

The dynamics of discrete-time computation, with application to recurrent neural networks and finite state machine extraction

Neural Computation