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...

SUMMARY Representing the concept of numerical data by linguistic rules is often desir­ able. In this paper, we present a novel rule-extraction algorithm from the radial basis function (RBF) neural network classifier for representing the hidden concept of numerical data. Gaussian function is used as the basis function of the RBF network. When training the RBF neural network, we allow for large o...

estimation of reservoir water saturation (sw) is one of the main tasks in well logging. many empirical equations are available, which are, more or less, based on archie equation. the present study is an application of radial basis function neural network (rbfnn) modeling for estimation of water saturation responses in a carbonate reservoir. four conventional petrophysical logs (pls) including d...

This paper describes a novel adaptive noise cancellation system with fast tunable radial basis function (RBF). The weight coefficients of the RBF network are adapted by the multi-innovation recursive least square (MRLS) algorithm. If the RBF network performs poorly despite of the weight adaptation, an insignificant node with little contribution to the overall performance is replaced with a new ...

Novel direct adaptive robust state and output feedback controllers are presented for the output tracking control of a class of nonlinear systems with unknown system dynamics and disturbances. Both controllers employ a variable-structure radial basis function (RBF) network that can determine its structure dynamically to approximate unknown system dynamics. Radial basis functions are added or rem...

Air pollution is a challenging issue in some of the large cities in developing countries. Air quality monitoring and interpretation of data are two important factors for air quality management in urban areas. Several methods exist to analyze air quality. Among them, we applied the dynamic neural network (TDNN) and Radial Basis Function (RBF) methods to predict the concentrations of ground-level...

We present a new high-order, local meshfree method for numerically solving reaction 5 diffusion equations on smooth surfaces of co-dimension one embedded in Rd. The novelty of the 6 method is in the approximation of the Laplace-Beltrami operator for a given surface using Hermite 7 radial basis function (RBF) interpolation over local node sets on the surface. This leads to compact 8 (or implicit...

This paper proposes an artificial neural network RBF to classification using feature descriptors. The theoretical and practical aspects of theory F distributions with different degrees of freedom introduced. The distribution F densities are similar in shape, making it difficult to identify the differences between the two densities. This paper is concerned with separating these same probability ...

This study deals with predicting nonlinear time history deflection of scallop domes subject to earthquake loading employing neural network technique. Scallop domes have alternate ridged and grooves that radiate from the centre. There are two main types of scallop domes, lattice and continuous, which the latticed type of scallop domes is considered in the present paper. Due to the large number o...

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...