Abstract In this paper, we solve nonlinear conservation laws using the radial basis function generated finite difference (RBF-FD) method. Nonlinear have solutions that entail strong discontinuities and shocks, which give rise to numerical instabilities when solution is approximated by a We introduce residual-based artificial viscosity (RV) stabilization framework adjusted RBF-FD method, where r...

Neural network process modelling needs the use of experimental design and studies. A new neural network constructive algorithm is proposed. Moreover, the paper deals with the influence of the parameters of radial basis function neural networks and multilayer perceptrons network in process modelling. Particularly, it is shown that the neural modelling, depending on learning approach, cannot be a...

energy management is one of the main ways of the efficient use of energy resources. the prediction of crop yields based on energy inputs can help farmers and policymakers to estimate the level of production. required data for study were randomly collected from 70 broiler farms in north west of iran. the input energies were included human labour, machinery, fuel, feed and electricity and the out...

This paper presents parallelization strategies for the Radial Basis Function-Finite Difference (RBFFD) method. As a generalized finite differencing scheme, the RBF-FD method functions without the need for underlying meshes to structure nodes. It offers high-order accuracy approximation but scales as O(N) per time step, with N being with the total number of nodes. To our knowledge, this is the f...

This paper presents experimental work on mammogram image analysis. Texture analysis is carried out using segmentation technique. Here, statistical method have been used to extract features from the segmented tumour area. The obtained features are classified using different classifiers such as Radial basis function, Main Feed forward and Main Fitnet method. The method was tested on 100 clinical ...

This paper presents the performance comparison of two architectures of neural networks: multi-layer perceptron (MLP) neural networks and radial basis function (RBF) neural networks on face recognition system (FRS). We are training MLP using different variants of back-propagation (BP) algorithm. AT&T database has been used for performance comparison. The BP is gradient descent based iterative al...

A complex radial basis function neural network is proposed for equalization of quadrature amplitude modulation (QAM) signals in communication channels. The network utilizes a sequential learning algorithm referred to as complex minimal resource allocation network (CMRAN) and is an extension of the MRAN algorithm originally developed for online learning in real-valued radial basis function (RBF)...

Radial Basis Function (RBF) neuron network is being applied widely in multivariate function regression. However, selection of neuron number for hidden layer and definition of suitable centre in order to produce a good regression network are still open problems which have been researched by many people. This article proposes to apply grid equally space nodes as the centre of hidden layer. Then, ...

This paper presents a method for use in estimating the state of charge (SOC) lithium-ion batteries which is based on an electrochemical impedance equivalent circuit model with controlled source. Considering that open-circuit voltage battery varies SOC, source proposed and current interact each other. On this basis, radial basis function (RBF) neural network adopted to estimate uncertainty onlin...

Abstract. Two dynamic mesh methods— Radial Basis Function (RBF) method and Delaunay method which have been used widely in fluid-structure interaction recently were compared. Primarily, the basic theory of these two methods and the dynamic mesh process were presented. Furthermore, using the variable bandwidth sparse matrix storage technique, the computational efficiency of the RBF method can be ...