A method is described for using Radial Basis Function (RBF) neural networks to generate a certainty factor reliability measure along with the network's normal output. The certainty factor approach is then compared with another technique for measuring RBF reliability, Parzen windows. Both methods are implemented into RBF networks, and the results of using each approach are compared. Advantages a...

In this paper, different methods for training radial basis function (RBF) networks for regression problems are described and illustrated. Then, using data from the DELVE archive, they are empirically compared with each other and with some other well known methods for machine learning. Each of the RBF methods performs well on at least one DELVE task, but none are as consistent as the best of the...

We define a generalized distance function on an unoriented 3D point set and describe how it may be used to reconstruct a surface approximating these points. This distance function is shown to be a Mahalanobis distance in a higher-dimensional embedding space of the points, and the resulting reconstruction algorithm a natural extension of the classical Radial Basis Function (RBF) approach. Experi...

Radial Basis Function (RBF) network based channel equalisers have a close relationship with Bayesian schemes. Decision feedback is introduced in the design of the RBF equaliser in order to reduce its computational complexity. The RBF Decision Feedback Equaliser (DFE) was found to give similar performance to the conventional mean square error (MSE) DFE over Gaussian channels using various Quadra...

Estimation of suspended sediments in rivers using soft computing techniques has been extensively performed around the world since 1990’s. However, accuracy in the results was always found to be highly desired and a profound crucial task. This study presents a thorough comparison between the performances of best basis function of Radial Basis Functions (RBF) and the best training algorithm in Mu...

Artificial neural network based equalizers can be used for equalization in coherent optical OFDM systems. The artificial neural network based multilayer layer perceptron is a feed-forward network consists of one hidden layer with one or more hidden nodes between its input and output layers and can be trained by using back propagation algorithm. However, this algorithm suffers from slow converge...

This paper describes an optimized training approach of radial basis function (RBF) classification by reducing a proposed classification-oriented error function. The training approach consists of two distinguished properties. First, radial basis functions, feature weights, and output weights can be updated iteratively; Second, it intrinsically distinguishes different learning contribution from t...

The purpose of this paper is to explore the representation capability of radial basis function (RBF) neural networks. The main results are: 1) the necessary and sufficient condition for a function of one variable to be qualified as an activation function in RBF network is that the function is not an even polynomial, and 2) the capability of approximation to nonlinear functionals and operators b...

introduction: in most bci articles which aim to separate movement imaginations, µ and β frequency bands have been used. in this paper, the effect of presence and absence of γ band on performance improvement is discussed since movement imaginations affect γ frequency band as well. methods: in this study we used data set 2a from bci competition iv. in this data set, 9 healthy subjects have perfor...

This analysis principally focuses on the implementation of Radial basis function (RBF) and back propagation (BPA) algorithms for training artificial neural network (ANN) to get the optimum mixture of Hydro fluorocarbon (HFC) and organic compound (Hydrocarbons) for obtaining higher coefficient of Performances (COPs). The thermodynamical properties of mixed refrigerants are observed using REFPROP...