F. Fallside We develop a sequential adaptation algorithm for radial basis function (RBF) neural networks of Gaussian nodes, based on the method of successive F-Projections. This method makes use of each observation efficiently in that the network mapping function so obtained is consistent with that information and is also optimal in the least L 2-norm sense. The RBF network with the F-Projectio...

The paper proposed a structure of Wireless Sensor Networks based Electronic-nose system to monitors air quality in the building. In the study, the authors researched a data processing algorithm: fuzzy neural network based on RBF(Radial Basis Function) network model, to quantitatively analyze the gas ingredient and put forward a routing protocol for the system.

A systematic method for the diagnosis of planar antenna arrays from far field radiation pattern using neural networks is presented. Two types of neural networks, Radial basis function (RBF) and Probabilistic neural network (PNN) are considered for the performance comparison. Deviation pattern is used as input to the neural network to determine the location of the faulty element and error in exc...

A novel reduced complexity Radial Basis Function (RBF) neural network based equaliser, referred to as the In-phase/Quadrature-phase RBF Equaliser (I/Q-RBFEQ), is proposed. The I/Q-RBF-EQ is employed in the context of turbo equalisation (TEQ) assisted by iterative channel estimation. The performance of the I/Q-RBF-TEQ is characterized in a noise limited environment over an equally weighted, symb...

We present a dual structural radial basis function (RBF) network for recursive function estimation. This network is a hybrid system which consists of two sub-RBF networks. One sub-network models the relationship between the current network output and the past ones, and the other one describes the relationship between the current network output and the inputs. We propose a new variant of extende...

The Bayesian equalizer is implementable by a proper employment of a radial basis function (RBF) neural network, with the inverse filtering problem posed as a classification problem. The proposed approach allows that the transmission of information and the RBF training be accomplished in a simultaneous and uninterrupted way. Moreover, the channel estimation procedure remains an unimodal optimiza...

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...

In this paper we present the capability of the Rank M-Type Radial Basis Function (RMRBF) neural network in the classification of Pap smear microscopic images. From simulation results we observe that the RMRBF neural network has better classification capabilities in comparison with other RBF based algorithms.

Classical Radial Basis Function (RBF) neural network controller designs typically fix the number of basis functions and tune only the weights. In this paper we present a backstepping neural network controller algorithm in which all RBF parameters, including centers, variances and weight matrices are tuned online. By using a Lyapunov approach, tuning rules for updating the RBF parameters are der...

Bio-absorbent palm fiber was applied for removal of cationic violet methyl dye from water solution. For this purpose, a solid phase extraction method combined with the artificial neural network (ANN) was used for preconcentration and determination of removal level of violet methyl dye. This method is influenced by factors such as pH, the contact time, the rotation speed, and the adsorbent dosag...