An integrated modeling and robust model predictive control (MPC) approach is proposed for a class of nonlinear systems. First, the nonlinear system is identified off-line by a RBF-ARX model possessing linear ARX model structure and state-dependent Gaussian RBF neural network type coefficients. On the basis of the RBF-ARX model, a combination of a local linearization model and a polytopic uncert...

A complex radial basis function neural network is proposed for equalization of quadrature amplitude modulation (QAM) signals in communication channels. The network utilizes a sequential learning algorithm referred to as complex minimal resource allocation network (CMRAN) and is an extension of the MRAN algorithm originally developed for online learning in real-valued radial basis function (RBF)...

Radial basis function (RBF) networks of Gaussian activation functions have been widely used in many applications due to its simplicity, robustness, good approximation and generalization ability, etc.. However, the training of such a RBF network is still a rather difficult task in the general case and the main crucial problem is how to select the number and locations of the hidden units appropri...

We examine the ARCH-GARCH models for the forecasting of the bond price time series provided by VUB bank and make comparisons the forecast accuracy with the class of RBF neural network models. A limited statistical or computer science theory exists on how to design the architecture of RBF networks for some specific nonlinear time series, which allows for exhaustive study of the underlying dynami...

ARTMAP neural network cla.c=;sifiers arc considered for the identification of radar emitter types from their waveform parameters. These classifiers can represent radar emitter type classes \vith one or more prototypes, perform on-line incremental learning to account for novelty encountered in the field, and process radar pulse streams at high speed, making them attractive for real-time applicat...

This paper is intender to be a simple example illustrating some of the capabilities of Radial basis function by pruning with QLP decomposition. The applicability of the radial basis function (RBF) type function of artificial neural networks (ANNS) approach for re-estimate the Box, Traingle, Epanechnikov and Normal densities. We propose an application of QLP decomposition model to reduce to the ...

A compact analog programmable multidimensional radialbasis-function (RBF)-based classifier is demonstrated in this chapter. The probability distribution of each feature in the templates is modeled by a Gaussian function that is approximately realized by the bell-shaped transfer characteristics of a proposed floating-gate bump circuit. The maximum likelihood, the mean, and the variance of the di...

For estimation of monthly precipitation, considering the intricacy and lack of accurate knowledge about the physical relationships, black box models usually are used because they produce more accurate values. In this article, a hybrid black box model, namely ANN-RBF, is proposed to estimate spatiotemporal value of monthly precipitation. In the first step a Multi Layer Perceptron (MLP) network i...

We propose a genetic algorithm for generating adversarial examples for machine learning models. Such approach is able to find adversarial examples without the access to model’s parameters. Different models are tested, including both deep and shallow neural networks architectures. We show that RBF networks and SVMs with Gaussian kernels tend to be rather robust and not prone to misclassification...

By a recent result on subperiodic trigonometric Gaussian quadrature, we construct a cubature formula of algebraic degree of exactness n on planar circular lenses (intersection of two overlapping disks) and “double bubbles” (union of two overlapping disks), with n2/2+O(n) nodes. An application is shown to RBF projection methods. 2000 AMS subject classification: 65D32.