# Optimized Hidden Markov Model based on Constrained Particle Swarm Optimization

arXiv: https://arxiv.org/abs/1811.03450 · HTML: https://mlsanity.com/paper/1811.03450/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Nov 2018.

## Built on by (most cited first, 1 of 1)

- [A New Algorithm for Hidden Markov Models Learning Problem](https://mlsanity.com/paper/2102.07112/) (2021)

## Abstract (arXiv)

As one of Bayesian analysis tools, Hidden Markov Model (HMM) has been used to in extensive applications. Most HMMs are solved by Baum-Welch algorithm (BWHMM) to predict the model parameters, which is difficult to find global optimal solutions. This paper proposes an optimized Hidden Markov Model with Particle Swarm Optimization (PSO) algorithm and so is called PSOHMM. In order to overcome the statistical constraints in HMM, the paper develops re-normalization and re-mapping mechanisms to ensure the constraints in HMM. The experiments have shown that PSOHMM can search better solution than BWHMM, and has faster convergence speed.