Oct 18, 2011 · The results demonstrate that the RHMM/PSO can provide meaningful insights for pattern recognition like fast lexicon-free recognition. It makes ...
We proposed a recurrent hidden Markov models (RHMM) and particle swarm optimisation (PSO) approach. Under this framework, convergence of local optimal solutions ...
A new method of HMM learning based on Particle Swarm Optimization (PSO) has been developed and the most reliable optimization approach in terms of ...
Missing: Recurrent | Show results with:Recurrent
This algorithm is based on a finite coding of the solution space based on the optimal trajectory of the state. It is applied to both discrete and continuous ...
Missing: Recurrent | Show results with:Recurrent
This paper presents new application of Particle Swarm Optimization (PSO) algorithm for training Hidden Markov Models (HMMs). The problem of finding an optimal ...
In the study reported in this paper, we used a hybrid algorithm combining particle swarm optimization with evolutionary algorithms to train HMMs for the ...
Missing: Recurrent | Show results with:Recurrent
Hybrid particle swarm optimisation algorithm is applied to image segmentation problem to determine the threshold in this paper. Based on the analysis of basic ...
Recurrent hidden Markov models using particle swarm optimisation · Zengshou DongYina GuoJ. Zeng. Computer Science. Int. J. Model. Identif. Control. 2011. TLDR.
Oct 22, 2024 · The paper presents new application of Particle Swarm Optimization for training Hidden Markov Models. The approach is verified on artificial data ...
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Jun 18, 2021 · We propose a hybrid schema applying hidden Markov models to recompute the parameter values into a super-swarm optimization method. Hidden Markov ...
Missing: Recurrent | Show results with:Recurrent