Maximum likelihood estimation for multivariate mixture observations of Markov chins
IEEE Transactions on Information Theory
Structure identification of fuzzy model
Fuzzy Sets and Systems
A unified approach to define fuzzy integrals
Fuzzy Sets and Systems
Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
The nature of mathematical modeling
The nature of mathematical modeling
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
New sequence processing algorithms using hidden markov models
New sequence processing algorithms using hidden markov models
Parameter determination for a generalized fuzzy model
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Cluster Analysis
Continuously variable duration hidden Markov models for automatic speech recognition
Computer Speech and Language
Modified Gath-Geva fuzzy clustering for identification of Takagi-Sugeno fuzzy models
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Generalized hidden Markov models. I. Theoretical frameworks
IEEE Transactions on Fuzzy Systems
Generalized hidden Markov models. II. Application to handwritten word recognition
IEEE Transactions on Fuzzy Systems
Q-measures: an efficient extension of the Sugeno λ-measure
IEEE Transactions on Fuzzy Systems
Structure identification of generalized adaptive neuro-fuzzy inference systems
IEEE Transactions on Fuzzy Systems
From a Gaussian mixture model to additive fuzzy systems
IEEE Transactions on Fuzzy Systems
From a Gaussian Mixture Model to Nonadditive Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Generalization of adaptive neuro-fuzzy inference systems
IEEE Transactions on Neural Networks
Adaptive pattern mining model for early detection of botnet-propagation scale
Security and Communication Networks
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We present a novel approach for the development of fuzzy hidden Markov models (FHMMs) by exploiting both additive and nonadditive properties of input fuzzy sets in the fuzzy rules of generalized fuzzy model (GFM). This development utilizes 1) Gaussian mixture model (GMM) to manipulate the mixture parameters for the input fuzzy sets and 2) GFM rules for the inclusion of states in the consequent part to be able to use HMM. Taking the components of Gaussian mixture density conditioned on the past system states and making use of equivalence of GMM with GFM, parameters of the additive and nonadditive FHMMs are estimated using the forward-backward procedure of the Baum-Welch algorithm. The additive and non additive FHMMs are validated on three benchmark applications involving time-series prediction, and the results are compared and found to be better than or equal to those of the existing recent fuzzy models.