This article demonstrates the application of least squares for the estimation of system parameters. Analytic as well as numerical approaches are described. The model of the system dynamics is assumed in the form of regression model and in the form of discrete impulse response. Solutions are discussed for the case of white noise and correlated noise corrupting the useful output signal of the sys...

We describe a new method for blind system identification that uses the cross relation properties between two or more sensor signals to estimate the impulse responsesof the channels. The method performs as well or better than other similar blind identification techniques under noisy and ill-conditioned channel conditions, and is computationally simpler to implement.

By an indirect control approach, an adaptive pole-placement control problem is considered for a scalar discrete-time linear plant assuming the knowledge of an upper bound of the plant order. A class of models that can be regarded IO be input-output equivalent to the plant is first constructed based on the parrrneter estimate generated by a least-squares-type identification scheme. A minimizatio...

Abstract: This paper presents an approach to the identification of nonlinear system in noisy environment using a wavelet based State Dependent Parameter (SDP) model to chacterize the system’s nonlinear dynamics. The obtained model is in the form of a set of linear regressive output/input terms (state) multiplied by the respective SDPs, which are compactly parameterized by wavelet basis function...

This paper proposed a method to identify nonlinear systems via the fuzzy weighted least squares support machine (FW-LSSVM). At first, we describe the proposed modeling approach in detail and suggest a fast learning scheme for its training. Because the training sample data of independent variable and dependent variable has a certain error, and we obtain the sample which has a certain fuzziness f...

We present a software package for structured total least-squares approximation problems. The allowed structures in the data matrix are block-Toeplitz, block-Hankel, unstructured, and exact. Combination of blocks with these structures can be specified. The computational complexity of the algorithms is O(m), where m is the sample size. We show simulation examples with different approximation prob...

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...

Recursive and least squares methods for identification of non-minimum-phase linear time-invariant (NMP-LTI) FIR systems are developed. The methods utilize the secondand third-order cumulants of the output of the FIR system whose input is an independent, identically distributed (i.i.d.) non-Gaussian process. Since knowledge of the system order is of utmost importance to many system identificatio...