Adaptive pattern classification for symbolic dynamic systems

This paper addresses pattern classification in dynamical systems, where the underlying algorithms are formulated in the symbolic domain and the patterns are constructed from symbol strings as probabilistic finite state automata (PFSA) with (possibly) diverse algebraic structures. A combination of Dirichlet and multinomial distributions is used to model the uncertainties due to the (finite-length) string approximation of symbol sequences in both training and testing phases of pattern classification. The classifier algorithm follows the structure of a Bayes model and has been validated on a simulation test bed. The results of numerical simulation are presented for several examples.

► Construction of probabilistic finite state automata from finite-length time series data.
► Construction of a Bayesian classifier for identification of the probability morph matrices.
► Quantification of inaccuracy due to finite-length approximation.
► Validation on a simulation test bed.