Add to ORKGAbstract. 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 computation-ally efficient and is capable of identifying parsimonious RBF networks that gen-eralise well. Moreover, the proposed algorithm is fully automatic and the user does not need to specify a termination criterion for the construction process.
A construction algorithm for multioutput radial basis function (RBF) network modelling is introduced...
Abstract. Input selection in the nonlinear function approximation is important and difficult problem...
The paper presents a novel two-layer learning method for radial basis function (RBP) networks. At th...
An orthogonal forward selection (OFS) algorithm based on the leave-one-out (LOO) criterion is propos...
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...
Input nodes of neural networks are usually predetermined by using a priori knowledge or selected by ...
In this review we bring together some of our recent work from the angle of the diversified RBF topol...
A construction algorithm for multioutput radial basis function (RBF) network modelling is introduced...
Abstract. Input selection in the nonlinear function approximation is important and difficult problem...
The paper presents a novel two-layer learning method for radial basis function (RBP) networks. At th...
An orthogonal forward selection (OFS) algorithm based on the leave-one-out (LOO) criterion is propos...
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...
Input nodes of neural networks are usually predetermined by using a priori knowledge or selected by ...
In this review we bring together some of our recent work from the angle of the diversified RBF topol...
A construction algorithm for multioutput radial basis function (RBF) network modelling is introduced...
Abstract. Input selection in the nonlinear function approximation is important and difficult problem...
The paper presents a novel two-layer learning method for radial basis function (RBP) networks. At th...