Modelling of Deterministic, Fuzzy and Probablistic Dynamical Systems

Recurrent neural networks and hidden Markov models have been the popular tools for sequence recognition problems such as automatic speech recognition. This work investigates the combination of recurrent neural networks and hidden Markov models into the hybrid architecture. This combination is feasible due to the similarity of the architectural dynamics of the two systems. Initial experiments were done by training recurrent neural networks to behave like finite-state automata using genetic algorithms in order to demonstrate that their structure is sufficiently rich to represent dynamical systems. The equivalence between these trained recurrent neural networks and corresponding automata is shown by extracting knowledge from the networks. The results show that hybrid recurrent neural networks can learn and represent dynamical systems such as finite automaton. Finally, the proposed hybrid architecture is applied to automatic speech phoneme recognition. The results show that hybrid recurrent neural networks perform with some conditions and degree of success when applied to difficult real-world problems.