A mathematical framework for possibility theory-based hidden Markov model. (2017)

• Record Type: Journal Article
• Authors: Baranwal, Neha; Nandi, G.C.
• Is Part Of: International journal of bio-inspired computation. Volume 10:Number 4(2018)
• Page Start: 239
• Page End: 247
• Publication Date: 2017
• Subjects: hidden Markov model -- possibility theory -- gesture recognition -- stochastic process; Biologically-inspired computing -- Periodicals; Computational biology -- Periodicals; 572.0285
• Languages: English
• ISSNs: 1758-0366

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.