Identification of nonlinear systems with missing data using stochastic neural network

In this paper, nonlinear identification is dealt with by using Gaussian sum distribution. This model is also called a stochastic neural network. By using the stochastic model, it is possible to estimate the output and also the missing elements in the input vector within the framework of conditional estimation. The model parameters can be estimated by using the EM algorithm. By interpolating the unknown elements, we don't have to discard the vectors including the missing elements.