Efficient interference cancellation often requires nonlinear processing of a reference signal. In this paper, hyper radial basis function (HRBF) neural networks for adaptive interference cancellation is developed. We show that the HRBF networks, with an appropriate learning algorithm, is able to approximate the interference signal more efficiently than standard radial basis function (RBF) netwo...

In this work, generalization ability of a hybrid neural network algorithm is investigated. This algorithm consists of a combination of Radial Basis Function (RBF) and Multilayer Perceptron (MLP) in one single network using conic section functions. The network architecture using this algorithm is called Conic Section Function Neural Network (CSFNN). Various problems are examined to demonstrate t...

We present a new derivative-free algorithm, ORBIT, for unconstrained local optimization of computationally expensive functions. A trust-region framework using interpolating Radial Basis Function (RBF) models is employed. The RBF models considered often allow ORBIT to interpolate nonlinear functions using fewer function evaluations than the polynomial models considered by present techniques. App...

Many types of radial basis functions, such as multiquadrics, contain a free parameter. In the limit where the basis functions become increasingly flat, the linear system to solve becomes highly ill-conditioned, and the expansion coefficients diverge. Nevertheless, we find in this study that limiting interpolants often exist and take the form of polynomials. In the 1-D case, we prove that with s...

Radial basis functions networks (RBF) with dynamic synapses are introduced. The novelty aspect consists in replacing the standard scalar values of the output weights by discrete-time FIR/IIR filters. LMS-type learning algorithms are derived and simulation results for prediction of chaotic time series are reported. ( 1998 Elsevier Science B.V. All rights reserved.

A parametric geometric metamodel is built for a nonlinear magnetostatic problem, using proper orthogonal decomposition (POD) approach combined with radial basis functions (RBFs) interpolation. Furthermore, the geometrical variation of problem modeled an RBF interpolation smooth mesh deformation. The applied single-phase EI inductance, and aim to create precise flux cartographies based on few so...

We propose a method for optimizing the complexity of Radial basis function (RBF) networks. The method involves two procedures: adaptation (training) and selection. The first procedure adaptively changes the locations and the width of the basis functions and trains the linear weights. The selection procedure performs the elimination of the redundant basis functions using an objective function ba...

1. Briefly summarize the paper’s contributions. Does it address a new problem? Does it present a new approach? Does it show new types of results?  [AS] This paper presents a fast approach to reconstruct smooth, manifold surfaces from point clouds and to repair incomplete meshes using polyharmonic Radial Basis Functions (RBFs), which were previously thought to be too slow to compute to be scala...

This paper proposes a robust algorithm for adaptive modelling of EEG signal classification using a modified Extended Kalman Filter (EKF). This modified EKF combines Radial Basis functions (RBF) and Autoregressive (AR) modeling and obtains better classification performance by truncating the filtering distribution when new observations are very informative.