Int. J. of Bio-Inspired Computation   »   2017 Vol.10, No.4

 

 

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.2016.10004307

 

Int. J. of Bio-Inspired Computation, 2017 Vol.10, No.4, pp.239 - 247

 

Available online: 07 Nov 2017

 

 

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