For multi-channel process, due to disadvantages of the open-loop single variable step method, multi-channel test method is used. That means all of the channels are tested at the same time. In order to eliminate cross-effect of the different test signals, it requires that all the test signals are uncorrelated. Several test signals are introduced and analyzed. Based on two familiar identification...

Abstract This paper presents an adaptive algorithm to estimate states and unknown parameters simultaneously for nonlinear time invariant systems which depend affinely on the unknown parameters. The system output signals are filtered and re-parameterized into a regression form from which the least squares error scheme is applied to identify the unknown parameters. The states are then estimated b...

This paper discusses the parameter estimation problems of multi-input output-error autoregressive (OEAR) systems. By combining the auxiliary model identification idea and the data filtering technique, a data filtering based recursive generalized least squares (F-RGLS) identification algorithm and a data filtering based iterative least squares (F-LSI) identification algorithm are derived. Compar...

There has been recent interest in using orthonormalised forms of fixed denominator model structures for system identification. A key motivating factor in the employment of these forms is that of improved numerical properties. Namely, for white input perfect conditioning of the least-squares normal equations is achieved by design. However, for the more usual case of coloured input spectrum, it i...

The methods of subspace system identification are extended to correlation function estimates, explicitly addressing the increase in computational difficulty of identifying input-tostate dynamics when correlation function estimates are used in place of input-output data for multivariable identification problems. It is shown that the regressor used to solve a common least-squares problem when ide...

In this paper, a wavelet based method is proposed to identify the constant coefficients of a second order linear system and is compared with the least squares method. The proposed method shows improved accuracy of parameter estimation as compared to the least squares method. Additionally, it has the advantage of smaller data requirement and storage requirement as compared to the least squares m...