Iterative identification algorithms for bilinear-in-parameter systems with autoregressive moving average noise

This paper considers the identification problems of a bilinear-in-parameter system with autoregressive moving average noise. The basic idea is to use the over-parameterization to transform a system into a linear regressive model, and to present a gradient based and a least squares based iterative algorithms for identifying the system parameters. The numerical simulation example is given to demonstrate the effectiveness of the proposed algorithms.