This paper studies the synchronization of SA and AV Node Oscillators using PSO optimized RBF-based controllers systems. High levels of control activities may excite unmodeled dynamics of a system. This matter changes the rules of controlling the system and achieving an acceptable control performance. In fact, the objective here is to reach a trade-off between tracking performance and parametric...

ANNARIMA that combines both autoregressive integrated moving average (ARIMA) model and artificial neural network (ANN) model is a valuable tool for modeling and forecasting nonlinear time series, yet the over-fitting problem is more likely to occur in neural network models. This paper provides a hybrid methodology that combines both radial basis function (RBF) neural network and auto regression...

In the fault diagnosis of the motor, the vibration signals can fully reflect the status of the motor. In this paper, on the basis of wavelet packet fault feature extraction, a new approach for motor fault diagnosis based on wavelet packet analysis and fuzzy RBF neural network was presented.The method gains the energy of characteristic channel of bearing failure vibration signals of asynchronous...

Radial fhndions (RBF) network is an important common place in neum-fnzzy systems. In particolar, their common dversal approximator properties make hrzzy systems as well as n e o d network systems excellent representations for system modelling. In data-based modelling, it is important that, the overfitting should be avoided to eohana the generslisstion cspabaty of the model since tbh is BII olti...

An accurate forecasting method for power generation of the wind energy conversion system (WECS) is urgently needed under the relevant issues associated with the high penetration of wind power in the electricity system. This paper proposes a hybrid method that combines orthogonal least squares (OLS) algorithm and genetic algorithm (GA) to construct the radial basis function (RBF) neural network ...

This paper presents a novel method for CT head image automatic segmentation. The images are obtained from patients having the spontaneous intra cerebral brain hemorrhage (ICH). The results of the segmentation are images partitioned into ve regions of interest corresponding to four tissue classes (skull, brain, calci cations and ICH ) and background. Once the images are segmented it is possible ...

In this paper, we propose a novel technique for blind image restoration and resolution enhancement based on radial basis function (RBF) neural network. The RBF network gives a solution of the regularization problem often seen in function estimation with certain standard smoothness functional used as stabilizers. A RBF network model is designed to represent the observed image. In this model, the...

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...

Learning in radial basis function (RBF) networks is the topic of this paper. Particularly we address the problem of intialisation the centers and scaling parameters in RBF networks utilizing classiication tree algorithms. This method was introduced by Kubat in 1998. Algorithms for the calculation of the centers and scaling parameters in an RBF network are presented and numerical results for the...