VEHICLES EMERGENCY RESPONSE SYSTEM ASSESSMENT BASED ON HIDDEN MARKOV MODEL

Abstract

The Hidden Markov Model (HMMs) provide a simple and efficient framework for modeling diffuse vector sequences that differ in terms of time. Whereas constant identification of the fundamental principles that underlie the HMM-based emergency response program, defining and simplifying the premises inherent in the direct application of those principles can lead to a low-precision and poorly operated environment visibility structure. Highly complex therefore is needed for the practical use of HMMs in contemporary structures. The purpose of this work is the implementation of the HMMs and holding experiments to find the optimal parameters models by the criterion of reducing the generalization error maximizing the probability of recognition samples and minimizing the probability recognition of false samples as applied to solving the problem of speech recognition. Such enhancements include the availability of functions, better simulation of covariance, measurement of differential parameters, correction and standardization, mitigation for noise, and a multiphase device mixture. The analysis concludes with a case study to demonstrate the mentioned techniques.