Hysteresis effect degrades the positioning accuracy of a piezostage, and hence the nonlinearity has to be suppressed for ultrahigh-precision positioning applications. This paper extends least squares support vector machines (LS-SVM) to the domain of hysteresis modeling and compensation for a piezostage driven by piezoelectric stack actuators. A LS-SVM model is proposed and trained by introducin...

Least Squares Support Vector Machines (LS-SVMs) were proposed by replacing the inequality constraints inherent to L1-SVMs with equality constraints. So far this idea has only been suggested for a least squares (L2) loss. We describe how this can also be done for the sumof-slacks (L1) loss, yielding a new classifier (Least 1-Norm SVMs) which gives similar models in terms of complexity and accura...

The paper presents a fuzzy least squares support vector machine (LS-FSVM) which is implemented with the help of neo-fuzzy neurons (NFN) and which is essentially a zero order Takagi-Sugeno fuzzy inference system. The proposed LS-FSVM-NFN is numerically simple because it’s generated with NFNs, it also has a small number of adjustable parameters and high speed associated with the possibility of ap...

Least Squares Support Vector Machines (LS-SVM) is a proven method for classification and function approximation. In comparison to the standard Support Vector Machines (SVM) it only requires solving a linear system, but it lacks sparseness in the number of solution terms. Pruning can therefore be applied. Standard ways of pruning the LSSVM consist of recursively solving the approximation problem...

The support vector machine (SVM) is a method for classification and for function approximation. This method commonly makes use of an /spl epsi/-insensitive cost function, meaning that errors smaller than /spl epsi/ remain unpunished. As an alternative, a least squares support vector machine (LSSVM) uses a quadratic cost function. When the LSSVM method is used for function approximation, a nonsp...

In order to diagnose incipient fault of analog circuits effectively, an analog circuit incipient fault approach by using kernel entropy component analysis (KECA) as a preprocessor is proposed in the paper. Time responses are acquired by sampling outputs of the circuits under test. Raw features with high dimension are generated by wavelet transform. Furthermore, lower dimensional features are pr...

In the present work we study the applicability of Support Vector Machines (SVMs) on the phoneme recognition task. Specifically, the Least Squares version of the algorithm (LS-SVM) is employed in recognition of the Greek phonemes in the framework of telephone-driven voice-enabled information service. The N-best candidate phonemes are identified and consequently feed to the speech and language re...

This paper has two objectives: (a) it describes the problem of finding a precise and uncomplicated model of a neutralisation process, (b) it details development of a nonlinear Model Predictive Control (MPC) algorithm for the plant. The model has a cascade Wiener structure, i.e. a linear dynamic part is followed by a nonlinear steady-state one. A Least-Squares Support Vector Machine (LS-SVM) app...