Particle Swarm Optimization For Hidden Markov Model

Hidden Markov Model (HMM) is a very well known method as a statistical model used for intelligent systems applications. Due to its involvement in various applications, it would be very important to have a good representation of HMM for the given problem to achieve good results. In this paper, we propose a theoretical approach that can be followed to obtain the best structure of HMM based on Particle Swarm Optimization (PSO) concepts. Given a set of comprehensive visible and invisible states, we propose a method based on PSO concepts to evolve an optimum HMM structure design. The proposed approach deals with two factors related to HMM, generating new states and updating probability values. The main steps followed in the proposed approach involve three main phases, the first phase is generating randomly a population of HMMs, the second phase is converting the generated HMM to PSO required format and the third phase is the application of PSO to find out the optimum HMM . The importance of the proposed approach over other previous approaches is that other approaches deal only with probability updating.