Recurrent hidden Markov models using particle swarm optimisation

Abstract

Hidden Markov models (HMMs) are well known for their application in temporal pattern recognition. However, they have such problems that easy to converge to local optimal solutions and not fit well with sparsely connected time-series data. It is therefore imperative to have good methods to explore a more suitable choice, which can avoid the problems mentioned above as much as possible. We proposed a recurrent hidden Markov models (RHMM) and particle swarm optimisation (PSO) approach. Under this framework, convergence of local optimal solutions and fit with sparsely connected time-series data are solved by adjusting disturbed extremum. Furthermore, PSO is used to train the RHMM and determine the model parameters. Our approach has been applied to cursive handwriting recognition. The results demonstrate that the RHMM/PSO can provide meaningful insights for pattern recognition like fast lexicon-free recognition. It makes lexicon-free result alternatives extraction efficiently, word hypotheses evaluation quickl...