An orthogonal forward selection (OFS) algorithm based on the leave-one-out (LOO) criterion is proposed for the construction of radial basis function (RBF) networks with tunable nodes. This OFS-LOO algorithm is computationally efficient and is capable of identifying parsimonious RBF networks that generalise well. Moreover, the proposed algorithm is fully automatic and the user does not need to specify a termination criterion for the construction process
Input nodes of neural networks are usually predetermined by using a priori knowledge or selected by ...
In this paper a new method for fast initialization of radial basis function (RBF) networks is propos...
The paper presents a novel two-layer learning method for radial basis function (RBP) networks. At th...
Abstract. An orthogonal forward selection (OFS) algorithm based on the leave-one-out (LOO) criterion...
An orthogonal forward selection (OFS) algorithm based on leave-one-out (LOO) criteria is proposed fo...
tWe develop an orthogonal forward selection (OFS) approach to construct radial basis function (RBF)n...
The radial basis function (RBF) network offers a viable alternative to the two-layer neural network ...
The construction of a radial basis function (RBF) network involves the determination of the model si...
The paper presents an approach for trainingmulti-output radial basis function (RBF) networksby combi...
New construction algorithms for radial basis function (RBF) network modelling are introduced based o...
We develop a particle swarm optimisation (PSO) aided orthogonal forward regression (OFR) approach f...
A novel particle swarm optimisation (PSO) tuned radial basis function (RBF) network model is propose...
An efficient data based-modeling algorithm for nonlinear system identification is introduced for rad...
A construction algorithm for multioutput radial basis function (RBF) network modelling is introduced...
In this review we bring together some of our recent work from the angle of the diversified RBF topol...
Input nodes of neural networks are usually predetermined by using a priori knowledge or selected by ...
In this paper a new method for fast initialization of radial basis function (RBF) networks is propos...
The paper presents a novel two-layer learning method for radial basis function (RBP) networks. At th...
Abstract. An orthogonal forward selection (OFS) algorithm based on the leave-one-out (LOO) criterion...
An orthogonal forward selection (OFS) algorithm based on leave-one-out (LOO) criteria is proposed fo...
tWe develop an orthogonal forward selection (OFS) approach to construct radial basis function (RBF)n...
The radial basis function (RBF) network offers a viable alternative to the two-layer neural network ...
The construction of a radial basis function (RBF) network involves the determination of the model si...
The paper presents an approach for trainingmulti-output radial basis function (RBF) networksby combi...
New construction algorithms for radial basis function (RBF) network modelling are introduced based o...
We develop a particle swarm optimisation (PSO) aided orthogonal forward regression (OFR) approach f...
A novel particle swarm optimisation (PSO) tuned radial basis function (RBF) network model is propose...
An efficient data based-modeling algorithm for nonlinear system identification is introduced for rad...
A construction algorithm for multioutput radial basis function (RBF) network modelling is introduced...
In this review we bring together some of our recent work from the angle of the diversified RBF topol...
Input nodes of neural networks are usually predetermined by using a priori knowledge or selected by ...
In this paper a new method for fast initialization of radial basis function (RBF) networks is propos...
The paper presents a novel two-layer learning method for radial basis function (RBP) networks. At th...