Abstract
AbstractTwo new types of fuzzy vector quantizationbased/hidden Markov models (FVQ/HMM)—multiplication‐type and addition‐type—are formulated to remove a mathematical inconsistency in conventional FVQ/HMMs proposed by Tseng et al. Experiments show the MTFVQ/HMM gives the best recognition rate among VQ‐type HMMs. Letting yt be an observation vector at time t, C1, …, CM be clusters to which yt is classified and si be the i‐the state of HMM, we show that MT‐FVQ/HMM is derivable by defining the occurrence degree of yt at si to be the negative of the Kullback‐Leibler divergence of a posteriori probability distribution {P(C1 | yt), … P(CM | yt)} from a priori probability distribution {P(C1 | si), …, P(CM | Si)}.
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