System identification using control theory

This paper considers preliminary results for a novel approach to the identification of finite-impulse response (FIR) or autoregressive (AR) models. Whereas traditional methods have employed a cost function such as least-squares or steepest descent, the new method uses deconvolution to split the unknown parameters from the regressors. This is achieved by using convolution in the feedback path of a high-gain control-system.