Recursive least-squares algorithm for a characteristic model with coloured noise by means of the data filtering technique

摘要

This work investigates the identification problems for a characteristic model with autoregressive moving average noise, and the sum of all coefficients of the model is equal to one. A recursive least-squares (RLS) algorithm using the data filtering technique is derived for the model. The basic idea is to use a linear filter to filter the input-output data, to decompose a characteristic model into a system model and a noise model, but the sum of all coefficients remains equalling to one. Moreover, the traditional RLS algorithm is also presented and compared with the proposed algorithm in terms of computational complexity and effectiveness. Finally, three numerical examples show that the proposed algorithm outperforms the conventional RLS algorithm.