A new algorithm, multiple concurrent recursive least squares (MCRLS) is developed for parameter estimation in a system having a set of governing equations describing its behavior that cannot be manipulated into a form allowing (direct) linear regression of the unknown parameters. In this algorithm, the single nonlinear problem is segmented into two or more separate linear problems, thereby enab...

Volterra lters have been applied to many nonlinear system identiication problems. However, obtaining good lter estimates from short and/or noisy data records is a diicult task. We propose a penalized least squares estimation algorithm and derive appropriate penalizing functionals for Volterra lters. An example demonstrates that penalized least squares estimation can provide much more accurate l...

Parameter estimation schemes based on least squares identification and detection ideas are proposed for ease of computation, reduced numerical difficulties, and bias reduction in the presence of colored noise correlated with the states of the signal generating system. The algorithms are simpler because in the edculations, the state vector is at one point replaced by a quantized version. This te...

Least-squares method is the most popular method for parameter estimation. It is easy applicable, but it has considerable drawback. Under well-known conditions in the presence of noise, the LS method produces asymptotically biased and inconsistent estimates. One way to overcome this drawback is the implementation of the instrumental variable method. In this paper several modifications of this me...

In a companion paper [1], we studied amplitude estimation of one-dimensional (1-D) sinusoidal signals from measurements corrupted by possibly colored observation noise. We herein extend the results for twodimensional (2-D) amplitude estimation, which is of interest in various applications, including medical imaging, synthetic aperture radar (SAR), seismology, and many others. In particular, we ...

The present study proposes a new radial basis function which is derived based on an idea of mapping data into a high dimensional feature space which is known as Reproducing Kernel Hilbert Space (RKHS) and then performing Radial Basis Function (RBF) network in the feature space. Orthogonal Least Squares (OLS) method is employed to select a suitable set of centers (regressors) from a large set of...

In this paper we discuss in detail a recently proposed kernel-based version of the recursive least-squares (RLS) algorithm for fast adaptive nonlinear filtering. Unlike other previous approaches, the studied method combines a sliding-window approach (to fix the dimensions of the kernel matrix) with conventional ridge regression (to improve generalization). The resulting kernel RLS algorithm is ...

Modelling, parameter identification, and simulation play an important role in systems biology. Usually, the goal is to determine parameter values that minimise the difference between experimental measurement values and model predictions in a least-squares sense. Large-scale biological networks, however, often suffer from missing data for parameter identification. Thus, the least-squares problem...

This paper focuses on the use of parameter estimation techniques for the implementation of real-time Fault Detection and Diagnosis schemes. A detailed analysis of the nonrecursive and recursive Least Squares methods is given in the context of the system diagnosis problem, and a procedure for performing fault detection and identification for multivariable systems is proposed. An application exam...

This paper considers the problem of adaptive identification of IIR systems when the system output is corrupted by noise. The standard recursive least squares algorithm is known to produce biased parameter estimates in this case. A new type of fast recursive identification algorithm is proposed which is built upon approximate inverse power iteration. The proposed adaptive algorithm can recursive...