Accurate and fast parameter identification of conditionally Gaussian Markov jump linear system with input control

Identifying the Markov jump systems accurately and rapidly is a challenging task due to the complexity of hidden state expectation exponentially increases along with the data length. This paper presents a special non-homogeneous and non-stationary linear Markov jump system with input control, where the hidden states are tractable, thus implementing optimal hidden state estimator is practical. A parameter identification algorithm relies on the optimal estimator and expectation–maximization (EM) algorithm is proposed for this special model, meanwhile, the local optima problem of EM is moderated via proper method. Numerical examples show the proposed algorithm can rapidly approximate the parameters that well describe the data, and outperforms other related approaches.