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...

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...

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 ...

We present a forecasting strategy based on stationary wavelet transform combined with radial basis function (RBF) neural network to improve the accuracy of 3-month-ahead hake catches forecasting of the fisheries industry in the central southern Chile. The general idea of the proposed forecasting model is to decompose the raw data set into an annual cycle component and an inter-annual component ...

A radial basis function (RBF) neural network approach with a fusion of multiple signal candidates in precision motion control is studied in this paper. Sensor weightages are assigned to sensor measurements according to the selector attributes and are approximated using RBF neural network in multi-sensor fusion. A specific application towards precision motion control of a linear motor system usi...

We establish versions of Descartes' rule of signs for radial basis function (RBF) neural networks. The RBF rules of signs provide tight bounds for the number of zeros of univariate networks with certain parameter restrictions. Moreover, they can be used to infer that the Vapnik-Chervonenkis (VC) dimension and pseudodimension of these networks are no more than linear. This contrasts with previou...

A radial basis function ( RBF ) neural network depends mainly upon an adequate choice of the number and positions of its basis function centers. In this paper we have proposed an algorithm for RBF neural network and the results may be reduced for artificial neural networks as particular cases.

In order to improve the precision of gyroscope, two decoupling method of DTG(Dynamic Tuned Gyroscope) were analyzed, the BP neural network and RBF network. The BP neural network has many advantages Compared to the traditional decoupling method, but still some drawbacks such as the over training, the congress process is very slow, and the hidden layer is also hard to determined. The paper introd...