Particle Swarm Optimization for Hidden Markov Models with application to Intracranial Pressure analysis
نویسندگان
چکیده
The paper presents new application of Particle Swarm Optimization for training Hidden Markov Models. The approach is verified on artificial data and further, the application to Intracranial Pressure (ICP) analysis is described. In comparison with Expectation Maximization algorithm, commonly used for the HMM training problem, the PSO approach is less sensitive on sticking to local optima because of its global character. However this advantage depends on character of the particular problem. The IC analysis is the case of such problem where it is suitable to use the PSO strategy. This is demonstrated by better classification result (85.1%) in comparison with the EM algorithm (76.3%).
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