In this paper, we deal with the problem of function approximation from a given set of input/output data. This problem consists of analyzing these training examples so that we can predict the output of the model given new inputs. We present a new method for function approximation of the I/O data using radial basis functions (RBFs). This approach is based on a new efficient method of clustering o...

This paper presents numerical solution of elliptic partial differential equations Poisson’s equation using a combination of logarithmic and multiquadric radial basis function networks. This method uses a special combination between logarithmic and multiquadric radial basis functions with a parameter r. Further, the condition number which arises in the process is discussed, and a comparison is m...

With the aim of classifying sperm whales, this report compares two methods that can use Gaussian functions, a radial basis function network, and support vector machines which were trained with two different approaches known as C-SVM and ν-SVM. The methods were tested on data recordings from seven different male sperm whales, six containing single click trains and the seventh containing a comple...

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...

In this paper, we present a direct solution method based on wavelet networks for image compression. Wavelet networks are a combination of radial basis function (RBF) networks and wavelet decomposition, where radial basis functions were replaced by wavelets. The results show that the wavelet networks approach succeeded to improve high performances in terms of compression ratio and reconstruction...

The existing Chinese Minorities OCR system is mainly oriented in the "literacy" level, the script recognition has not attracted the attention it deserves, and the area of recognizing the kinds of Chinese minority scripts is still in a blank. The method of recognizing the kinds of Chinese minority scripts based on wavelet analysis and Radial Basis Function Network (RBFN) is presented which adopt...

In this paper, a new approach for the compensation of unknown periodic disturbances by means of a neural network is presented. The neural controller supports the conventional controller by suppressing periodic disturbances. This is done by online learning in order to adapt to different operating conditions and to time varying unknown disturbances. The neural network learns an optimal compensati...

| Adaptive learning dynamics of the Radial Basis Function Network (RBFN) are compared with a scale-based clustering technique Won93] and a relationship between the two is pointed out. Using this link, it is shown how scale-based clustering can be done using the RBFN, with the Radial Basis Function (RBF) width as the scale parameter. The technique suggests the \right" scale at which the given da...

An essential requirement to create an accurate classifier ensemble is the diversity among the individual base classifiers. In this paper, Multi-View Forests, a method to construct ensembles of tree-structured radial basis function (RBF) networks using multi-view learning is proposed. In Multi-view learning it is assumed that the patterns to be classified are described by multiple feature sets (...

Three methods for improving the performance of (gaussian) radial basis function (RBF) networks were tested on the NETtaik task. In RBF, a new example is classified by computing its Euclidean distance to a set of centers chosen by unsupervised methods. The application of supervised learning to learn a non-Euclidean distance metric was found to reduce the error rate of RBF networks, while supervi...