Title: A mathematical framework for possibility theory-based hidden Markov model

Authors: Neha Baranwal; G.C. Nandi

Addresses: Robotics and Artificial Intelligence Laboratory, Indian Institute of Information Technology, Allahabad, India ' Robotics and Artificial Intelligence Laboratory, Indian Institute of Information Technology, Allahabad, India

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.

Keywords: hidden Markov model; possibility theory; gesture recognition; stochastic process.

DOI: 10.1504/IJBIC.2017.087920

International Journal of Bio-Inspired Computation, 2017 Vol.10 No.4, pp.239 - 247

Available online: 07 Nov 2017

Full-text access for editors Access for subscribers Purchase this article Comment on this article