This paper describes a novel adaptive noise cancellation system with fast tunable radial basis function (RBF). The weight coefficients of the RBF network are adapted by the multi-innovation recursive least square (MRLS) algorithm. If the RBF network performs poorly despite of the weight adaptation, an insignificant node with little contribution to the overall performance is replaced with a new ...

The automatic depth control electrohydraulic system of a certain minesweeping tank is complex nonlinear system, and it is difficult for the linear model obtained by first principle method to represent the intrinsic nonlinear characteristics of such complex system. This paper proposes an approach to construct accurate model of the electrohydraulic system with RBF neural network trained by geneti...

A stable adaptive control scheme for multi-point mooring system (MPMS) with uncertain dynamics is proposed in this paper. The designed by a hybrid controller based on RBF (Radial Basis Function) NN (Neural Network) and SMC (Sliding Mode Control), which learns the MPMS dynamic changes, compensation of external disturbances realized through RBFNN control. Meanwhile RBF-SMC parameters are adapted ...

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...

In this paper, a nonlinear offline model of the solid oxide fuel cell (SOFC) is built by using a radial basis function (RBF) neural network based n a genetic algorithm (GA). During the process of modeling, the GA aims to optimize the parameters of RBF neural networks and the optimum alues are regarded as the initial values of the RBF neural network parameters. Furthermore, we utilize the gradie...

In this paper, a new algorithm is described for on-line identification and adaptive control of MIMO affine nonlinear systems having unknown dynamics a priori, by using a nonlinearly parameterized additive recurrent neural network (ARNN). The ARNN uses the radial basis functions (RBF) as activation functions. However, some adjustable parameters (centers and variances) in RBF appear nonlinearly a...

Brushless DC(BLDC) motors are widely used for many industrial applications, In view of the problem that it is difficult to tune the parameters and get satisfied control characteristics by using normal conventional PID controller. a online identification method based on Radial Basis Function(RBF) has been proposed in this paper. In this method, connection weight of neural network was revised in ...

| This paper presents a fast orthogo-nalization process to train a Radial Basis Function (RBF) neural network. The traditional methods for connguring the RBF weights is to use some matrix inversion or iterative process. These traditional approaches are either time consuming or computationally expensive, and often do not converge to a solution. The goal of this paper is rst to use a fast orthogo...