This research proposes a pattern/shape-similarity-based clustering approach for time series prediction. This article uses single hidden Markov model (HMM) for clustering and combines it with soft computing techniques (fuzzy inference system/artificial neural network) for the prediction of time series. Instead of using distance function as an index of similarity, here shape/pattern of the sequen...

The paper presents a new variant of parameter estimation methods for discrete hidden Markov models(HMM) in speech recognition. This method makes use of a codeword dependent distribution normalization(CDDN) and a distance weighting by fuzzy contribution in dealing with the problems of robust state modeling in a FVQ based modeling. The proposed method is compared with the existing techniques usin...

In state mixture modelling (SMM), the temporal structure of the observation sequences is represented by the state joint probability distribution where mixtures of states are considered. This technique is considered in an iterative scheme via maximum likelihood estimation. A fuzzy estimation approach is also introduced to cooperate with the SMM model. This new approach not only saves calculation...

This paper intends to introduce a simple fast space-efficient linear method for a general pattern recognition problem. The presented algorithm can find the closest match for a given sample within a number of samples which has already been introduced to the system. The fact of using averaging and fuzzy numbers in this method encourages that it may be a noise resistant recognition process. As a t...

Sub-wavelength nanostructured systems with tunable electromagnetic properties, such as hyperbolic metamaterials (HMMs), provide a useful platform to tailor spontaneous emission processes. Here, we investigate system comprising Eu3+(NO3)3·6H2O nanocrystals on an HMM structure featuring hexagonal array of Ag nanowires in porous Al2O3 matrix. The HMM-coupled Eu3+ ions exhibit up 2.4 fold increase ...