According to literature, it is well-known that the training algorithm of RBF neural networks depends a lot by the specific way to obtain the positioning of RBF centers over the input data space, and to fit the neural weights to the output layer, respectively. Having as starting point a real pattern recognition task belonging to video imagery to solve, this paper presents a comparative analysis ...

Accurate forecast of rainfall has been one of the most important issues in hydrological research. Due to rainfall forecasting involves a rather complex nonlinear data pattern; there are lots of novel forecasting approaches to improve the forecasting accuracy. In this paper, a new approach using the Modular Radial Basis Function Neural Network (M–RBF–NN) technique is presented to improve rainfal...

The performance of a Radial Basis Functions network (RBF) can be increased with the use of an ensemble of RBF networks because the RBF networks are successfully applied to solve classification problems and they can be trained by gradient descent algorithms. Reviewing the bibliography we can see that the performance of ensembles of Multilayer Feedforward (MF) networks can be improved by the use ...

Classical work in model reference adaptive control for uncertain nonlinear dynamical systems with a Radial Basis Function (RBF) neural network adaptive element does not guarantee that the network weights stay bounded in a compact neighborhood of the ideal weights when the system signals are not Persistently Exciting (PE). Recent work has shown, however, that an adaptive controller using specifi...

This paper presents a comparison between two Artificial Neural Network (ANN) approaches, namely, Multilayer Perceptron (MLP) and Radial Basis Function (RBF) networks, in flood forecasting. The basic difference between the two methods is that the parameters of the former network are nonlinear and those of the latter are linear. The optimum model parameters are therefore guaranteed in the latter,...

In this article an attempt is made to study the applicability of a general purpose, supervised feed forward neural network with one hidden layer, namely. Radial Basis Function (RBF) neural network. It uses relatively smaller number of locally tuned units and is adaptive in nature. RBFs are suitable for pattern recognition and classification. Performance of the RBF neural network was also compar...

A neural network-based model reference adaptive control approach (MRAC) for ship steering systems is proposed in this paper. For the nonlinearities of ship steering system, performances of traditional adaptive control algorithms are not satisfactory in fact. The presented MRAC system utilizes RBF neural network to approximate the unknown nonlinearities in order to get a high adaptive control pe...

The appropriate operation of a radial basis function (RBF) neural network depends mainly upon an adequate choice of the parameters of its basis functions. The simplest approach to train an RBF network is to assume fixed radial basis functions defining the activation of the hidden units. Once the RBF parameters are fixed, the optimal set of output weights can be determined straightforwardly by u...

In a radial basis function (RBF) network, the RBF centers and widths can be evolved by a cooperative-competitive genetic algorithm. The set of genetic strings in one generation of the algorithm represents one REP network, not a population of competing networks. This leads to moderate computation times for the algorithm as a whole. Selection operates on individual RBFs rather than on whole netwo...

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...