This paper proposes an enhanced RBF network that enhances learning algorithms between input layer and middle layer and between middle layer and output layer individually for improving the efficiency of learning. The proposed network applies ART2 network as the learning structure between input layer and middle layer. And the autotuning method of learning rate and momentum is proposed and applied...

A neural network model based on a chaotic particle swarm optimization (CPSO) radial basis function-back propagation (RBF-BP) was suggested to improve the accuracy of reactor temperature prediction. The training efficiency RBF-BP is influenced some degree by large randomness initial weight and threshold. To address impact threshold uncertainty combined network, this paper proposes using algorith...

With the analysis of the technology of phase space reconstruction, a modeling and forecasting technique based on the Radial Basis Function (RBF) neural network for chaotic time series is presented in this paper. The predictive model of chaotic time series is established with the adaptive RBF neural networks and the steps of the chaotic learning algorithm with adaptive RBF neural networks are ex...

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...

A complex radial basis function neural network is proposed for equalization of quadrature amplitude modulation (QAM) signals in communication channels. The network utilizes a sequential learning algorithm referred to as complex minimal resource allocation network (CMRAN) and is an extension of the MRAN algorithm originally developed for online learning in real-valued radial basis function (RBF)...

A radial basis function ( RBF ) neural network depends mainly upon an adequate choice of the number and positions of its basis function centers. In this paper we have proposed an algorithm for RBF neural network and the results may be reduced for artificial neural networks as particular cases.

This paper presents the MRBF network, a new algorithm adapted from the RBF network, to construct the classifiers for predicting phenotypic resistance on 6 protease inhibitors. The performance of the prediction was measured by 10-fold cross-validation. The results show that MRBF gives the lowest average mean square error (MSE) when compared with the traditional RBF network and multiple linear re...

This paper presents a modified RBF network with additional linear input connections together with a hybrid training algorithm. The training algorithm is based on kmeans clustering with square root updating method and Givens least squares algorithm with additional linear input connections features. Two real data sets have been used to demonstrate the capability of the proposed RBF network archit...

This paper proposes a radial basis function (RBF) based approach for the fuel injection control problem. In the past, neural controllers for this problem have centred on using a cerebellar model articulation controller (CMAC) type network with some success. The current production engine control units also use look-up tables in their fuel injection controllers, and if adaptation is permitted to ...

Radial Basis Function (RBF) neuron network is being applied widely in multivariate function regression. However, selection of neuron number for hidden layer and definition of suitable centre in order to produce a good regression network are still open problems which have been researched by many people. This article proposes to apply grid equally space nodes as the centre of hidden layer. Then, ...