The performance of radial basis function decision feedback equalised burst-by-burst adaptive modulation is presented for transmission over dispersive wideband mobile channels. Radial Basis Function (RBF) network based channel equalisers have a close relationship with Bayesian schemes. The RBF decision feedback equaliser (RBF DFE) is capable of estimating the 'short term bit error rate' of the r...

We present a new high-order, local meshfree method for numerically solving reaction 5 diffusion equations on smooth surfaces of co-dimension one embedded in Rd. The novelty of the 6 method is in the approximation of the Laplace-Beltrami operator for a given surface using Hermite 7 radial basis function (RBF) interpolation over local node sets on the surface. This leads to compact 8 (or implicit...

in this paper, the method of differentiating asthmatic and non-asthmatic patients using the  frequency analysis of  capnogram  signals is presented.  previously, manual study on capnogram signal has been conducted  by several researchers. all past researches showed significant correlation between capnogram signals and asthmatic patients. however all of them are just manual study conducted throu...

In this paper, load-carrying capacity in steel shear wall (SSW) was estimated using artificial neural networks (ANNs). The SSW parameters including load-carrying capacity (as ANN’s target), plate thickness, thickness of stiffener, diagonal stiffener distance, horizontal stiffener distance and gravity load (as ANN’s inputs) are used in this paper to train the ANNs. 144 samples data of each of th...

A Radial Basis Function ( RBF ) neural network can be regarded as a feed forward network composed of multiple layers of neurons with entirely different roles. The input layer made of sensory units that connect the network to its environment. A radial basis function neural network depends mainly upon an adequate choice of the number and positions of its basis function centers. In case of general...

We present a new constructive algorithm for building Radial-Basis-Function (RBF) network classiiers and a tree based associated algorithm for fast processing of the network. This method, named Constructive Tree Radial-Basis-Function (CTRBF), allows to build and train a RBF network in one pass over the training data set. The training can be in supervised or unsupervised mode. Furthermore, the al...

Conventionally, a radial basis function (RBF) network is constructed by obtaining cluster centers of basis function by maximum likelihood learning. This paper proposes a novel learning algorithm for the construction of radial basis function using sensitivity analysis. In training, the number of hidden neurons and the centers of their radial basis functions are determined by the maximization of ...

Abstract In this research, a radial basis function artificial neural network (RBF-ANN) model was developed to predict the hot deformation flow curves of API X65 pipeline steel. The results of the developed model was compared with the results of a new phenomenological model that has recently been developed based on a power function of Zener-Hollomon parameter and a third order polynomial functio...

A Radial Basis Function ( RBF ) neural network can be regarded as a feed forward network composed of multiple layers of neurons with entirely different roles. The input layer made of sensory units that connect the network to its environment. A radial basis function neural network depends mainly upon an adequate choice of the number and positions of its basis function centers. In case of general...