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Computation of Entropy of Most Likelihood State Sequence Obtained from Non Homogeneous Fuzzy Hidden Markov Chain


Paper Type 
 Contributed Paper
Title 
 Computation of Entropy of Most Likelihood State Sequence Obtained from Non Homogeneous Fuzzy Hidden Markov Chain
Author 
 Sujatha Ramalingam and Rajalaxmi Thasari Murali
Email 
 laxmi.raji18@gmail.com, sujathar@ssn.edu.in
Abstract:

 The entropy of a possibilistic variable provides a measure of its uncertainty. An algorithm is proposed for computing the entropy of the most likelihood state sequence obtained from the Viterbi algorithm for Non Homogeneous Fuzzy Hidden Markov Chain (NHFHMC) which is a bivariate discrete process, where  is a non homogeneous fuzzy Markov chain on possibility space and  is the sequence of observations  such that the conditional possibility distribution of  only depends on  [8]. The Viterbi algorithm for NHFHMC is the algorithm for tracking the most likelihood hidden states of a process from a sequence of observations. An important problem while tracking a process is estimating the uncertainty present in the solution. To overcome this kind of uncertainty we have computed the entropy associated with that most likelihood state sequence and this entropy measure is given in triangular fuzzy number.
Start & End Page 
 1019 - 1030
Received Date 
 2013-12-10
Revised Date 
Accepted Date 
 2014-06-11
Full Text 
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Keyword 
 Triangular fuzzy number, Possibility Space, Conditional possibility, Non - Homogeneous Fuzzy Markov Chain, Fuzzy Hidden Markov Chain, Entropy
Volume 
 Vol.42 No.4 (OCTOBER 2015)
DOI 
Citation 
 Ramalingam S. and Thasari R., Computation of Entropy of Most Likelihood State Sequence Obtained from Non Homogeneous Fuzzy Hidden Markov Chain, Chiang Mai Journal of Science, 2015; 42(4): 1019-1030.
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