Difficulty was known to get satisfactory measurement effect on precision in capacitive grain’s moisture measurement due to many influencing factors, such as temperature, species, compaction and so on. The data confusion method of Radial Basis Function (RBF) nerve network is adopted. With improved orthogonal optimal method, the RBF nerve network’s weight factors can be obtained. This method can ...

Promising numerical results using once and twice integrated radial basis functions have been recently presented. In this work we investigate the integrated radial basis function (IRBF) concept in greater detail, connect to the existing RBF theory, and make conjectures about the properties of IRBF approximation methods. The IRBF methods are used to solve PDEs. c © 2006 Elsevier Science Ltd. All ...

Based on radial basis function neural network (RBF NN),the paper proposed a new algorithm for strip shape recognition. Compared with back propagation (BP) algorithm and improved least squares method (LSM), RBF NN shows excellent overall performance, such as learning speed, recognition precision and anti-interference capability. Copyright©2005IFAC Keyword: Strip Shape, Pattern Recognition, RBF,B...

Radial Basis Function (RBF) methods that employ infinitely differentiable basis functions featuring a shape parameter are theoretically spectrally accurate methods for scattered data interpolation and for solving Partial Differential Equations. It is also theoretically known that RBF methods are most accurate when the linear systems associated with the methods are extremely ill-conditioned. Thi...

‎Radial basis function method has been used to handle linear and‎ ‎nonlinear equations‎. ‎The purpose of this paper is to introduce the method of RBF to‎ ‎an existing method in solving nonlinear two-level iterative‎ ‎techniques and also the method is implemented to four numerical‎ ‎examples‎. ‎The results reveal that the technique is very effective‎ ‎and simple. Th...

Metamodeling for High Dimensional Simulation-based Design Problems Songqing Shan Dept. of Mech. and Manuf. Engineering University of Manitoba Winnipeg, MB, Canada R3T 5V6 [email protected] G. Gary Wang School of Engineering Science Simon Fraser University Surrey, BC, Canada V3T 0A3 [email protected] Abstract Computational tools such as finite element analysis and simulation are widely used i...

Two optimization aspects of the meshless method (MLM) based on nonsingular radial basis functions (RBFs) are considered in an acoustic indoor problem. The former is minimization mean value relative error solution domain. letter at selected points In both cases leads to finding relations between physical parameters and approximate parameters. room field with uniform, impedance walls considered. ...

In this paper the neural network based lter for nonlinear interference cancellation is developed. The Hyper Radial Basis Function (HRBF) network with associated Manhattan learning algorithm is proposed for non-linear noise cancellation under assumption that reference noise is available. The HRBF network is a generalization of radial basis function (RBF) and generalized radial basis function (GR...