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...

An integrated modeling and robust model predictive control (MPC) approach is proposed for a class of nonlinear systems. First, the nonlinear system is identified off-line by a RBF-ARX model possessing linear ARX model structure and state-dependent Gaussian RBF neural network type coefficients. On the basis of the RBF-ARX model, a combination of a local linearization model and a polytopic uncert...

This paper presents an approach to model the nonlinear dynamic behaviors of the Automatic Depth Control Electrohydraulic System (ADCES) of a certain mine-sweeping weapon using Radial Basis Function (RBF) neural networks. In order to obtain accurate RBF neural networks efficiently, a hybrid learning algorithm is proposed to train the neural networks, in which centers of neural networks are optim...

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.