we compare performance of adaptive schemes which are based on radial-basis functions and kalman filters for fast extraction of auditory evoked potentials. moreover, we propose a new method based on evoked potential modeling in the kalman filter framework, which can improve the accuracy compared to the existing methods. simulation results show that adaptive schemes and the kalman method are not ...

Meshfree methods with discontinuous radial basis functions and their numerical implementation for elastic problems are presented. We study the following radial basis functions: the multiquadratic (MQ), the Gaussian basis functions and the thin-plate basis functions. These radial basis functions are combined with step function enrichments directly or with enriched Shepard functions. The formulat...

In this paper, q-Gaussian Radial Basis Functions are presented as an alternative to Gaussian Radial Basis Function. This model is based on q-Gaussian distribution, which parametrizes the Gaussian distribution by adding a new parameter q. The q-Gaussian Radial Basis Function allows different Radial Basis Functions to be represented by updating the new parameter q. For example, when the q-Gaussia...

AhtractThis paper discusses the rationale for employing alternative basis functions to the ubiquitous Gaussian in Radial Basis Function networks. In particular we concentrate upon employing unbounded basis functions (though the network as a whole remains bounded), and non positive definite basis functions. The use of unbounded and nonpositive basis functions, though counterintuitive in applicat...

In this paper, we propose a methodology for training a new model of artificial neural network called the generalized radial basis function (GRBF) neural network. This model is based on generalized Gaussian distribution, which parametrizes the Gaussian distribution by adding a new parameter t. The generalized radial basis function allows different radial basis functions to be represented by upda...

Radial basis function networks (RBF) are efficient general function approximators. They show good generalization performance and they are easy to train. Due to theoretical considerations RBFs commonly use Gaussian activation functions. It has been shown that these tight restrictions on the choice of possible activation functions can be relaxed in practical applications. As an alternative differ...

This paper proposes a Radial Basis Function Neural Network (RBFNN) which reproduces different Radial Basis Functions (RBFs) by means a real parameter q, named q-Gaussian RBFNN. The architecture, weights and node topology are learnt through a Hybrid Algorithm (HA) with the iRprop+ algorithm as the local improvement procedure. In order to test its overall performance, an experimental study with e...