Abstract:This paper proposes a new strategy for fitting Hidden Markov Models to error processes of channels with memory. Our approach consists of obtaining the analytical expression of the likelihood function of the model parameters and applying particle swarm optimization (PSO) to obtain their maximum likelihood (ML) estimates. In particular, this approach is here applied to the well known single error-state (simplified) Fritchman models, which have been recognized as a very useful tool for modeling error process of s… Show more
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