In this paper, a neural sliding mode control approach is developed to adjust the gain using radial basis function (RBF) network (NN) for tracking of Microelectromechanical Systems (MEMS) triaxial vibratory gyroscope. First with fixed proposed assure asymptotic stability closed loop system. Then RBF derived gradient method in switching law. With adaptive learning network, chattering phenomenon e...

Radial basis function (RBF) based approximation methods for numerical solution of partial differential equations are interesting due to their potentially spectral accuracy and due to being meshfree. This could be especially beneficial for high dimensional problems, where meshing is non-trivial. In this work, we present different RBF approaches and evaluate them on a multi-asset option pricing p...

This paper presents a training method for radial basis function neural network (RBFNN) based on GA and dynamic optimal learning rate. Genetic algorithm (GA) is applied to search for the optimal centers from input space of the RBF. Dynamic optimal learning rate approach is also appended to the genetic algorithms’ searching procession to obtain a set of weighting factors so that the network actua...

A novel approach, radial basis function (RBF) mixed with domain decomposition method (DDM) based on Galerkin finite element method (FEM) has been introduced in our previous work. The proposed method divides the computational domain into a series of rectangular sub-domains, and each sub-domain is taken as a separate calculation area to get the solution expression and shape function by using the ...

Based on the recently developed Finite Integration Method (FIM) for solving one-dimensional ordinary and partial differential equations, this paper extends the technique to higher dimensional partial differential equations. The main idea is to extend the first order finite integration matrices constructed by using either Ordinary Linear Approach (OLA) (uniform distribution of nodes) or Radial B...

Level set methods have become an attractive design tool in shape and topology optimization for obtaining lighter and more efficient structures. In this paper, the popular Radial Basis Functions (RBFs) in scattered data fitting and function approximation are incorporated into the conventional level set methods to construct a more efficient approach for structural topology optimization. RBF impli...

To effectively organize and analyze massive Web information, in this paper, we have designed a Web classification mining system. BP network has lots of disadvantages, so we have proposed a method that uses RBFNN (Radial Basis Function Neural Network) to classify the text information in Web pages. In this paper, the model of classification system mainly includes RBF (Radial Basis Function) class...

We present a new numerical simulation framework for prediction of flow patterns in the human left ventricle model. In this study, radial basis function (RBF) mesh morphing method is developed and applied within finite-volume computational fluid dynamics (CFD) approach. The simulations are designed to closely mimic details recent tomographic particle image velocimetry (TomoPIV) experiments. nume...

Facial recognition and expression analysis is rapidly becoming an area of intense interest in computer science and humancomputer interaction design communities. The most expressive way humans display emotions is through facial expressions. In this paper skin and non-skin pixels were separated. Face regions were extracted from the detected skin regions. Facial expressions are analyzed from facia...