EEG 시퀀스 분석을 위한 HSA 기반 HMM 구조 최적화

Hidden Markov Models (HMMs) are widely used for biological signal, such as neural sequence, analysis because of their ability to incorporate sequential information in their structure. An automatic means of optimizing the structure of HMMs would be highly desirable. In this paper, we explore the possibility of using a Harmony Search (HS) algorithm for optimizing the HMM structure. HS is sufficiently adaptable to allow incorporation of other techniques like Baum-Welch training algorithm. A HMM training strategy using HS is proposed, and it is tested on finding optimized structure for the pattern recognition of EEG sequence. The proposed HS for HMM can performs global searching without initial parameter setting, local optima, and solution divergence.