We present a forecasting strategy based on stationary wavelet transform combined with radial basis function (RBF) neural network to improve the accuracy of 3-month-ahead hake catches forecasting of the fisheries industry in the central southern Chile. The general idea of the proposed forecasting model is to decompose the raw data set into an annual cycle component and an inter-annual component ...

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...

A radial basis function (RBF) neural network approach with a fusion of multiple signal candidates in precision motion control is studied in this paper. Sensor weightages are assigned to sensor measurements according to the selector attributes and are approximated using RBF neural network in multi-sensor fusion. A specific application towards precision motion control of a linear motor system usi...

We establish versions of Descartes' rule of signs for radial basis function (RBF) neural networks. The RBF rules of signs provide tight bounds for the number of zeros of univariate networks with certain parameter restrictions. Moreover, they can be used to infer that the Vapnik-Chervonenkis (VC) dimension and pseudodimension of these networks are no more than linear. This contrasts with previou...

In this paper, a novel two-stage Improved Radial Basis Function (IRBF) neural network technique is proposed to predict the joint damage of a fifty member frame structure with semi-rigid connections in both frequency and time domain. The effective input patterns as normalized design signature indices (NDSIs) in frequency domain and acceleration responses in time domain are simulated numerically ...

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.

Dynamic Voltage Scaling requires a model of the critical path to allow the proper setting of the supply voltage to meet a target frequency. In this paper, we propose a new technique to model the supply and delay relationship of a critical path which is based on Gaussian Radial Basis Functions (RBF) neural network. This modeling technique is able to successfully capture the process and temperatu...

In order to improve the precision of gyroscope, two decoupling method of DTG(Dynamic Tuned Gyroscope) were analyzed, the BP neural network and RBF network. The BP neural network has many advantages Compared to the traditional decoupling method, but still some drawbacks such as the over training, the congress process is very slow, and the hidden layer is also hard to determined. The paper introd...

This paper presents a new neural network based controller design for multivariable systems. The proposed controller is designed using radial basis function (RBF) neural network. Weight update equation using classical least mean square principle is derived for the RBF network. The controller generates optimal control signals abiding by constraints, if any, on the control signals. Simulation resu...

In this paper we investigate alternative designs of a Radial Basis Function Network acting as classifier in a face recognition system. Input to the RBF network is the projections of a face image over the principal components. A database of 250 facial images of 25 persons is used for training and evaluation. Two RBF designs are studied: the forward selection and the gaussian mixture model. Both ...