A Dynamic Parameter Tuning Algorithm For Rbf Neural Networks

The objective of this thesis is to present a methodology for fine-tuning the parameters of radial basis function (RBF) neural networks, thus improving their performance. Three main parameters affect the performance of an RBF network. They are the centers and widths of the RBF nodes and the weights associated with each node. A gridded center and orthogonal search algorithm have been used to initially determine the parameters of the RBF network. A parameter tuning algorithm has been developed to optimize these parameters and improve the performance of the RBF network. When necessary, the recursive least square solution may be used to include new nodes to the network architecture. To study the behavior of the proposed network, six months of real data at fifteen-minute intervals has been collected from a North American pulp and paper company. The data has been used to evaluate the performance of the proposed network in the approximation of the relationship between the optical properties of base sheet paper and the process variables. The experiments have been very successful and Pearson correlation coefficients of up to 0.98 have been obtained for the approximation. The objective of this thesis is to present a methodology for fine-tuning the parameters of radial basis function (RBF) neural networks, thus improving their performance. Three main parameters affect the performance of an RBF network. They are the centers and widths of the RBF nodes and the weights associated with each node. A gridded center and orthogonal search algorithm have been used to initially determine the parameters of the RBF network. A parameter tuning algorithm has been developed to optimize these parameters and improve the performance of the RBF network. When necessary, the recursive least square solution may be used to include new nodes to the network architecture. To study the behavior of the proposed network, six months of real data at fifteen-minute intervals has been collected from a North American pulp and paper company. The data has been used to evaluate the performance of the proposed network in the approximation of the relationship between the optical properties of base sheet paper and the process variables. The experiments have been very successful and Pearson correlation coefficients of up to 0.98 have been obtained for the approximation. The objective of this thesis is to present a methodology for fine-tuning the parameters of radial basis function (RBF) neural networks, thus improving their performance. Three main parameters affect the performance of an RBF network. They are the centers and widths of the RBF nodes and the weights associated with each node. A gridded center and orthogonal search algorithm have been used to initially determine the parameters of the RBF network. A parameter tuning algorithm has been developed to optimize these parameters and improve the performance of the RBF network. When necessary, the recursive least square solution may be used to include new nodes to the network architecture. To study the behavior of the proposed network, six months of real data at fifteen-minute intervals has been collected from a North American pulp and paper company. The data has been used to evaluate the performance of the proposed network in the approximation of the relationship between the optical properties of base sheet paper and the process variables. The experiments have been very successful and Pearson correlation coefficients of up to 0.98 have been obtained for the approximation

Forecasting the geomagnetic activity of the Dst Index using radial basis function networks

The Dst index is a key parameter which characterises the disturbance of the geomagnetic field in magnetic storms. Modelling of the Dst index is thus very important for the analysis of the geomagnetic field. A data-based modelling approach, aimed at obtaining efficient models based on limited input-output observational data, provides a powerful tool for analysing and forecasting geomagnetic activities including the prediction of the Dst index. Radial basis function (RBF) networks are an important and popular network model for nonlinear system identification and dynamical modelling. A novel generalised multiscale RBF (MSRBF) network is introduced for Dst index modelling. The proposed MSRBF network can easily be converted into a linear-in-the-parameters form and the training of the linear network model can easily be implemented using an orthogonal least squares (OLS) type algorithm. One advantage of the new MSRBF network, compared with traditional single scale RBF networks, is that the new network is more flexible for describing complex nonlinear dynamical systems

NARX-based nonlinear system identification using orthogonal least squares basis hunting

An orthogonal least squares technique for basis hunting (OLS-BH) is proposed to construct sparse radial basis function (RBF) models for NARX-type nonlinear systems. Unlike most of the existing RBF or kernel modelling methods, whichplaces the RBF or kernel centers at the training input data points and use a fixed common variance for all the regressors, the proposed OLS-BH technique tunes the RBF center and diagonal covariance matrix of individual regressor by minimizing the training mean square error. An efficient optimization method isadopted for this basis hunting to select regressors in an orthogonal forward selection procedure. Experimental results obtained using this OLS-BH technique demonstrate that it offers a state-of-the-art method for constructing parsimonious RBF models with excellent generalization performance

Radial basis function network learns ceramic processing and predicts related strength and density

Radial basis function (RBF) neural networks were trained using the data from 273 Si3N4 modulus of rupture (MOR) bars which were tested at room temperature and 135 MOR bars which were tested at 1370 C. Milling time, sintering time, and sintering gas pressure were the processing parameters used as the input features. Flexural strength and density were the outputs by which the RBF networks were assessed. The 'nodes-at-data-points' method was used to set the hidden layer centers and output layer training used the gradient descent method. The RBF network predicted strength with an average error of less than 12 percent and density with an average error of less than 2 percent. Further, the RBF network demonstrated a potential for optimizing and accelerating the development and processing of ceramic materials

Improved streamflow forecasting using self-organizing radial basis function artificial neural networks

Streamflow forecasting has always been a challenging task for water resources engineers and managers and a major component of water resources system control. In this study, we explore the applicability of a Self Organizing Radial Basis (SORB) function to one-step ahead forecasting of daily streamflow. SORB uses a Gaussian Radial Basis Function architecture in conjunction with the Self-Organizing Feature Map (SOFM) used in data classification. SORB outperforms the two other ANN algorithms, the well known Multi-layer Feedforward Network (MFN) and Self-Organizing Linear Output map (SOLO) neural network for simulation of daily streamflow in the semi-arid Salt River basin. The applicability of the linear regression model was also investigated and concluded that the regression model is not reliable for this study. To generalize the model and derive a robust parameter set, cross-validation is applied and its outcome is compared with the split sample test. Cross-validation justifies the validity of the nonlinear relationship set up between input and output data. © 2004 Elsevier B.V. All rights reserved