Adaptive communication is one of the methods used for high rate communication with efficient spectrum efficiency and with improved accuracy for future wireless communication systems. In this paper, we propose an adaptive modulated Orthogonal Frequency Division Multiplexing (OFDM) system based on Radial Basis Function (RBF) and then their performance (MSE) and classification accuracy is evaluate...

Bayesian methods have been successfully applied to regression and classification problems in multi-layer perceptrons. We present a novel application of Bayesian techniques to Radial Basis Function networks by developing a Gaussian approximation to the posterior distribution which, for fixed basis function widths, is analytic in the parameters. The setting of regularization constants by crossval...

In this paper we use a genetic algorithm (GA) for selecting the initial seed points (prototypes, kernels) for a Radial Basis Function (RBF) classifier. The chromosome is directly mapped onto the training set and represents a subset: it contains 1 at the ith position if the ith element of the set is included, and 0, otherwise. Thus the GA serves a condensing technique that can hopefully lead to ...

This work presents a procedure for the estimation of a two-mass vocal fold model starting from a time-varying target ow signal. The model is specied by a large number of physical parameters, computed as functions of four articulatory parameters (three laryngeal muscle activations and subglottal pressure). Flow waveforms synthesized by the model are characterized by means of a set of typical v...

This paper describes the use of a radial basis function (RBF) neural network. It approximates the process parameters for the extrusion of a rubber profile used in tyre production. After introducing the problem, we describe the RBF net algorithm and the modeling of the industrial problem. The algorithm shows good results even using only a few training samples. It turns out that the „curse of dim...

This paper proposes a novel computational intelligence technique, based on the sociological concept of human group formation, with the aim to acquire a better solution to classification problems. The key concept of the human group formation is about the behavior of in-group members that try to unite with their own group as much as possible, and at the same time maintain social distance from the...

A RBF network was used as an empirical modeling tool. Results on simulated processes show that such a network can learn the shape of the function reasonably well with very limited experimental data. The use of the RBF network model is also illustrated with real experimental data from a soil extraction process.

When faced with unpredictable environments, the human motor system has been shown to develop optimized adaptation strategies that allow for online adaptation during the control process. Such online adaptation is to be contrasted to slower over-trial learning that corresponds to a trial-by-trial update of the movement plan. Here we investigate the interplay of both processes, i.e., online adapta...

The design of radial basis function networks (RBF) is rather complex because of the great number of parameters that must be adjusted : positioning and number of kernels, choice of the distance type and centre widths, weight values. This article details these points in the framework of classification tasks with a partitioning approach : the global K-class problem is split into K 2-class sub-prob...

This paper describes a face recognition system that uses partial face images (for example, eye, nose, and ear images) for input data. The recognition technique is based on using Radial Basis Function (RBF) networks. As compared with using a standard backpropagation (BP) learning algorithm, the RBF networks are far superior for the face recognition task. From the experimental results of face rec...