This work describes a Radial Basis Function (RBF) neural network method used to analyze ECG signals for diagnosing cardiac arrhythmias effectively. The proposed method can accurately classify and differentiate normal (Normal) and abnormal heartbeats. Abnormal heartbeats include left bundle branch block (LBBB), right bundle branch block (RBBB), atrial premature contractions (APC) and premature v...

Adaptive communication is one of the methods used for high rate communication with efficient spectrum efficiency and with improved accuracy for future wireless communication systems. In this paper, we propose an adaptive modulated Orthogonal Frequency Division Multiplexing (OFDM) system based on Radial Basis Function (RBF) and then their performance (MSE) and classification accuracy is evaluate...

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...

In the present study, prediction of Alumina recovery efficiency (A.R.E), the amount of produced red mud (A.P.R), red mud settling rate (R.S.R) and bound-soda losses (B.S.L) in Bayer process red mud has been carried out for the first time in the field. These predictions are based on Lime to bauxite ratio and chemical analyses of bauxite and lime as Bayer process feed materials. Radial basis func...

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...

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...

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 novel numerical method for solving ordinary differential equations using radial basis function (RBF) network with extreme learning machine algorithm. A single-layer RBF link neural model has been developed the proposed method. The weight from hidden layer to output can be calculated efficiently by experimental comparison of various methods proves that shows better performance than ...

Studies convergence properties of radial basis function (RBF) networks for a large class of basis functions, and reviews the methods and results related to this topic. The authors obtain the network parameters through empirical risk minimization. The authors show the optimal nets to be consistent in the problem of nonlinear function approximation and in nonparametric classification. For the cla...

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...