Prediction of the production rate of the cutting dimensional stone process is crucial, especially when chain saw machines are used. The cutting dimensional rock process is generally a complex issue with numerous effective factors including variable and unreliable conditions of the rocks and cutting machines. The Group Method of Data Handling (GMDH) type of neural network and Radial Basis Functi...

The two well-known neural network, Hopfield networks and Radial Basis Function networks, have different structures and characteristics. Hopfield neural network and RBF neural network are two of the most commonly-used types of feedback networks and feedforward networks respectively. This study gives an overview for Hopfield neural network and RBF neural network in architectures, the learning pro...

1 Implementing Radial Basis Functions Using Bump-Resistor Networks John G. Harris University of Florida EE Dept., 436 CSE Bldg 42 Gainesville, FL 32611 [email protected] .edu Abstract| Radial Basis Function (RBF) networks provide a powerful learning architecture for neural networks [6]. We have implemented a RBF network in analog VLSI using the concept of bump-resistors. A bump-resistor is a ...

Two approaches were explored which integrate neural net classifiers with Hidden Markov Model (HMM) speech recognizers. Both attempt to improve speech pattern discrimination while retaining the temporal processing advantages of HMMs. One approach used neural nets to provide second-stage discrimination following an HMM recognizer. On a small vocabulary task, Radial Basis Function (RBF) and back-p...

The paper outlines a mixture distribution of Gaussian method for estimation the probability density function. A RBF neural network architecture for realising such estimation is proposed. Moreover, the learning algorithm is derived. The practical use of the method is illustrated by a small example of an recognition application. The aim of which is to recognise vehicles based on the acoustical si...

In this article an attempt is made to study the applicability of a general purpose, supervised feed forward neural network with one hidden layer, namely. Radial Basis Function (RBF) neural network. It uses relatively smaller number of locally tuned units and is adaptive in nature. RBFs are suitable for pattern recognition and classification. Performance of the RBF neural network was also compar...

We develop forecasting models based on the neural approach for the forecasting of the bond price time series provided by the VUB bank and make their comparisons of the forecast accuracy with the class of the statistical ARCH-GARCH models. There is a limited statistical or computer science theory on how to design the architecture of the RBF networks for some specific nonlinear financial or econo...

Differential Global Positioning System (DGPS) provides differential corrections for a GPS receiver in order to improve the navigation solution accuracy. DGPS position signals are accurate, but very slow updates. Improving DGPS corrections prediction accuracy has received considerable attention in past decades. In this research work, the Neural Network (NN) based on the Gaussian Radial Basis Fun...

In this study, a radial basis function (RBF) neural network with three-layer feed forward architecture was developed to effectively predict the viscosity ratio of different ethylene glycol/water based nanofluids. A total of 216 experimental data involving CuO, TiO2, SiO2, and SiC nanoparticles were collected from the published literature to train and test the RBF neural network. The parameters ...