NARX-based nonlinear system identification using orthogonal least squares basis hunting

An orthogonal least squares technique for basis hunting (OLS-BH) is proposed to construct sparse radial basis function (RBF) models for NARX-type nonlinear systems. Unlike most of the existing RBF or kernel modelling methods, whichplaces the RBF or kernel centers at the training input data points and use a fixed common variance for all the regressors, the proposed OLS-BH technique tunes the RBF center and diagonal covariance matrix of individual regressor by minimizing the training mean square error. An efficient optimization method isadopted for this basis hunting to select regressors in an orthogonal forward selection procedure. Experimental results obtained using this OLS-BH technique demonstrate that it offers a state-of-the-art method for constructing parsimonious RBF models with excellent generalization performance

DC Motor Friction Identification With ANFIS and LS-SVM Method / Muhammad Zaiyad Ismail ... [et al.]

Friction has been an old age problem for any motion system to accomplish its optimum performance. Friction compensation has been identified as an effective strategy to enhance the performance of a motion system. To be able to compensate the friction in motors, the friction itself needs to be identified. Through the latest development in Artificial Intelligent, it has been obvious that the major Artificial Intelligent-paradigms are able to resemble any nonlinear functions precisely and hence, being used as one approach in friction modeling and identification. In this paper, a DC motor is selected as the representative of simple motor. A real-time experiment involving a DC motor is required in getting the best velocity to friction torque relationship. By using MatLab, the friction modeling data is trained with two different methods, which are Adaptive Neuro-Fuzzy Inference System (ANFIS) and Least Squares Support Vector Machine (LS-SVM). The performance of both methods is compared and analysed

A new kernel-based approach for overparameterized Hammerstein system identification

In this paper we propose a new identification scheme for Hammerstein systems, which are dynamic systems consisting of a static nonlinearity and a linear time-invariant dynamic system in cascade. We assume that the nonlinear function can be described as a linear combination of $p$ basis functions. We reconstruct the $p$ coefficients of the nonlinearity together with the first $n$ samples of the impulse response of the linear system by estimating an $np$-dimensional overparameterized vector, which contains all the combinations of the unknown variables. To avoid high variance in these estimates, we adopt a regularized kernel-based approach and, in particular, we introduce a new kernel tailored for Hammerstein system identification. We show that the resulting scheme provides an estimate of the overparameterized vector that can be uniquely decomposed as the combination of an impulse response and $p$ coefficients of the static nonlinearity. We also show, through several numerical experiments, that the proposed method compares very favorably with two standard methods for Hammerstein system identification.Comment: 17 pages, submitted to IEEE Conference on Decision and Control 201