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...

Networks based on basis set function expansions, such as the Radial Basis Function (RBF), or Separable Basis Function (SBF) networks, have non-linear parameters that are not trivial to optimize. Clustering techniques are frequently used to optimize positions for localized functions. Context-dependent fuzzy clustering techniques improve convergence of parameter optimization, leading to better ne...

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...

In Bishop-style constructive algebra it is known that if a module over a commutative ring has a Noetherian basis function, then it is Noetherian. Using countable choice we prove the reverse implication for countable and strongly discrete modules. The Hilbert basis theorem for this specific class of Noetherian modules, and polynomials in a single variable, follows with Tennenbaum’s celebrated ve...