In this paper, two feed forward neural network models have been presented to predict the Silicon Modification Level (SiML) of W319 aluminum alloys using the Thermal Analysis (T.A) parameters as inputs. The developed neural networks are a Multilayer Perceptron (MLP) network and a Radial Basis Function (RBF) network. The neural network models were found to predict the SiML accurately (R=0.99). Th...

We review the use of feed-forward networks as estimators of probability densities in hidden Markov modelling. In this paper we are mostly concerned with radial basis functions (RBF) networks. We note the isomorphism of RBF networks to tied mixture density estimators; additionally we note that RBF networks are trained to estimate posteriors rather than the likelihoods estimated by tied mixture d...

A RBF network was used as an empirical modeling tool. Results on simulated processes show that such a network can learn the shape of the function reasonably well with very limited experimental data. The use of the RBF network model is also illustrated with real experimental data from a soil extraction process.

Accurate forecast of rainfall has been one of the most important issues in hydrological research. Due to rainfall forecasting involves a rather complex nonlinear data pattern; there are lots of novel forecasting approaches to improve the forecasting accuracy. In this paper, a new approach using the Modular Radial Basis Function Neural Network (M–RBF–NN) technique is presented to improve rainfal...

Standard particle swarm optimization (SPSO) algorithm was modified by escape strategy of the particle velocity, and an escape PSO (EPSO) was proposed to overcome the shortcomings of being trapped in local optima because of premature convergence. To enhance the performance of radial basis function (RBF) neural network, the EPSO is combined with RBF neural network to form a EPSON hybrid algorithm...

The retinoblastoma gene Rb is the prototype tumor suppressor and is conserved in Drosophila. We use the developing fly retina as a model system to investigate the role of Drosophila Rb (rbf) during differentiation. This report shows that mutation of rbf and rhinoceros (rno), which encodes a PHD domain protein, leads to a synergistic delay in photoreceptor cell differentiation in the developing ...

In this paper, we propose a novel technique for blind image restoration and resolution enhancement based on radial basis function (RBF) neural network. The RBF network gives a solution of the regularization problem often seen in function estimation with certain standard smoothness functional used as stabilizers. A RBF network model is designed to represent the observed image. In this model, the...

Modelling Molten Carbonate Fuel Cells (MCFC) is very difficult and the existing models are too complicated to be used for controlling design, especially for on-line control design. This paper presents the application of neural networks identification method to develop the nonlinear temperature model of MCFC stack. The hidden layer units of the neural networks consist of a set of nonlinear radia...

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 ...

We have developed a new algorithm, RBF-MREIT, for Magnetic Resonance Electrical Impedance Tomography (MREIT) by applying the radial basis function (RBF) network and Simplex method. RBF-MREIT uses only one component of the measured magnetic flux density to reconstruct the conductivity images, and provides a solution to the rotation problem in MREIT. The proposed algorithm is tested on a three-sp...