Bayesian method for identification of constrained nonlinear processes with missing output data

Abstract

A methodology for the identification of nonlinear models using constrained particle filters under the scheme of the expectation-maximization (EM) algorithm is presented in this paper. Missing or irregularly sampled observations are commonplace in the chemical industry. In order to circumvent the difficulties rendered by largely incomplete data set, an improved EM based algorithm, which uses the expected value of the log-likelihood function including the missing observations, is developed. Constrained particle filters are adopted to solve the expected log-likelihood function in the EM algorithm. The efficiency of the proposed method in handling missing data is illustrated through numerical examples and validated through experiments.