A mathematical framework for possibility theory-based hidden Markov model
by Neha Baranwal; G.C. Nandi
International Journal of Bio-Inspired Computation (IJBIC), Vol. 10, No. 4, 2017

Abstract: Exploring correct pattern from low frequency time series data is challenging. In resolving this problem, the concept of possibility theory-based hidden Markov model (PTBHMM) has been proposed. In this paper, all the three fundamental problems (evaluation, decoding and learning) of conventional HMM have been addressed using possibility theory. For handling uncertainty, we have used axiomatic approach of possibility theory. Time complexity of existing solutions of HMM (forward, backward, Viterbi, Baum Welch) and proposed possibility-based solutions have been calculated and compared. From comparison result, it has been found that PTBHMM has lesser time complexity and hence will be more suitable for real-time applications.

Online publication date: Fri, 10-Nov-2017

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