A strategy for detection of feedstock variations in a continuous pulp digester is presented. A Gaussian Radial Basis Function Neural Network is used to infer these unmeasured variations. The absence of plant data motivates the development of training set data. The efficiency and limitation of the approach is demonstrated with a first principles model.

This paper presents a model reference adaptive PD control scheme based on RBF neural network for the greenhouse climate control problem. A model of nonlinear conservation laws of enthalpy and matter between numerous system variables affecting the greenhouse climate is used to validate the proposed control scheme. Compared with the conventional adaptive PD control scheme based on RBF neural netw...

In this paper we provide a short overview of the Radial Basis Functions (RBF), their properties, the motivations behind their use and some of their applications. RBF’s have been employed for functional approximation in time-series modeling and in pattern classification. They have been shown to implement the Bayesian rule and to model any continuous inputoutput mapping. RBF’s are embedded in a t...

This paper presents a novel technique which may be used to determine an appropriate threshold for interpreting the outputs of a trained Radial Basis Function (RBF) classifier. Results from two experiments demonstrate that this method can be used to improve the performance of RBF classifiers in practical applications.

Neural networks with radial basis functions method are used to solve a class of initial boundary value of fractional partial differential equations with variable coefficients on a finite domain. It takes the case where a left-handed or right-handed fractional spatial derivative may be present in the partial differential equations. Convergence of this method will be discussed in the paper. A num...