An Improved Hybrid Algorithm for Optimizing the Parameters of Hidden Markov Models
Abukari Abdul Aziz Danaa, Mohammed Ibrahim Daabo, Alhassan Abdul-Barik
Asian Journal of Research in Computer Science · pp. 63–73 · Published 25 Jun 2021
10.9734/ajrcos/2021/v10i130235Abstract
Hidden Markov Models (HMMs) have become increasingly popular in the last several years due to the fact that, the models are very rich in mathematical structure and hence can form the theoretical basis for use in a wide range of applications. Various algorithms have been proposed in literature for optimizing the parameters of these models to make them applicable in real-life. However, the performance of these algorithms has remained computationally challenging largely due to slow/premature convergence and their sensitivity to preliminary estimates. In this paper, a hybrid algorithm comprising the Particle Swarm Optimization (PSO), Baum-Welch (BW), and Genetic Algorithms (GA) is proposed and implemented for optimizing the parameters of HMMs. The algorithm not only overcomes the shortcomings of the slow convergence speed of the PSO but also helps the BW escape from local optimal solution whilst improving the performance of GA despite the increase in the search space. Detailed experimental results demonstrates the effectiveness of our proposed approach when compared to other techniques available in literature.
References (10)
- 1 A tutorial on hidden Markov models and selected applications in speech recognition [DOI]
- 2 A Comparison of Particle Swarm Optimization and the Genetic Algorithm [DOI]
- 3 A best-path-updating information-guided ant colony optimization algorithm [DOI]
- 4 A Novel Genetic Algorithm Based on Tabu Search for HMM Optimization [DOI]
- 5 HMM parameter optimization using tabu search [DOI]
- 6 Particle Swarm Optimization For Hidden Markov Model [DOI]
- 7 A New Improved Simplified Particle Swarm Optimization Algorithm [DOI]
- 8 An Effective Hybrid Optimization Algorithm for HMM [DOI]
- 9 Optimization of HHM by a genetic algorithm
- 10 Optimized Hidden Markov Model based on Constrained Particle Swarm Optimization [DOI]
Cited by 0
No indexed citations yet.
Related research
- Attitude of Final Year Medical Students and House Officers to Otorhinolaryngology Surgery Training — shares topic coverage
- Nigerian Athletes’ Previous Training, Practice and Current Knowledge of Bystander Cardiopulmonary Resuscitation — shares topic coverage
- Effect of Direct Attention Training on Aggressiveness among Healthcare Workers in Kogi State, Nigeria — shares topic coverage
- Awareness of Hygiene and Safety Practices on Raw Milk Produced and Consumed in Kiambu County, Kenya — shares topic coverage
- Impact of Training on Rural Chicken Production Reared By Women in Islamabad/ Rawalpindi, Pakistan — shares topic coverage
Article metrics
Real usage data collected on this platform.
16
Page views
0
PDF downloads
1
Outbound clicks
0
Citations
Views over time
Views by country
Approximate, from request IP at view time — not citizenship or institution. Countries with fewer than 5 views are grouped as "Other".
Traffic sources
Referring site, by host.
Views and downloads exclude known bots/crawlers. Citations combines this platform's own DOI-resolved index with each external source's own reported total — see Cited by above for individually listed citing works. Last refreshed 0 seconds ago.