| This paper presents a fast orthogo-nalization process to train a Radial Basis Function (RBF) neural network. The traditional methods for connguring the RBF weights is to use some matrix inversion or iterative process. These traditional approaches are either time consuming or computationally expensive, and often do not converge to a solution. The goal of this paper is rst to use a fast orthogo...

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...

The use of the K-means algorithm and the K-nearest neighbor heuristic in estimating the radial basis function (RBF) parameters may produce sub-optimal performance when the input vectors contain correlated components. This paper proposes to overcome this problem by incorporating full covariance matrices into the RBF structure and to use the expectation-maximi-zation (EM) algorithm to estimate th...

Speaker identification is the computing task to identify an unknown identity based on the voice. A good speaker identification system must have a high accuracy rate to avoid invalid identity. Despite of last few decades’ efforts, accuracy rate in speaker identification is still low. In this paper, we propose a hybrid approach of unsupervised and supervised learning i.e. subtractive clustering a...

In this paper, a neural sliding mode control approach is developed to adjust the gain using radial basis function (RBF) network (NN) for tracking of Microelectromechanical Systems (MEMS) triaxial vibratory gyroscope. First with fixed proposed assure asymptotic stability closed loop system. Then RBF derived gradient method in switching law. With adaptive learning network, chattering phenomenon e...

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...

A high performance speaker-independent isolated-word hybrid speech recognizer was developed which combines Hidden Markov Models (HMMs) and Radial Basis Function (RBF) neural networks. In recognition experiments using a speaker-independent E-set database, the hybrid recognizer had an error rate of 11.5% compared to 15.7% for the robust unimodal Gaussian HMM recognizer upon which the hybrid syste...

Radial Basis Function (RBF) neuron network is being applied widely in multivariate function regression. However, selection of neuron number for hidden layer and definition of suitable centre in order to produce a good regression network are still open problems which have been researched by many people. This article proposes to apply grid equally space nodes as the centre of hidden layer. Then, ...

This work addresses an autonomous underwater vehicle (AUV) for applying nonlinear control which is capable of disturbance rejection via intelligent estimation of uncertainties. Adaptive radial basis function neural network (RBF NN) controller is proposed to approximate unknown nonlinear dynamics. The problem of designing an adaptive RBF NN controller was augmented with sliding mode robust term ...

This paper presents the problem of multiple quadrature amplitude modulated signals equalization and argues the use of a radial basis functions neural network (RBF-NN) equalizer. Different competitive learning algorithms for the RBF-NN centres determination are discussed. A new competitive learning algorithm is introduced, the rival penalized competitive learning, which rewards the winner and pe...