Radial Basis Probabilistic Neural Network (RBPNN) demonstrates broader and much more generalized capabilities which have been successfully applied to different fields. In this paper, the RBPNN is extended by calculating the Euclidean distance of each data point based on a kernel-induced distance instead of the conventional sum-of squares distance. The kernel function is a generalization of the ...

tthe uncertainty estimation and compensation are challenging problems for the robust control of robot manipulators which are complex systems. this paper presents a novel decentralized model-free robust controller for electrically driven robot manipulators. as a novelty, the proposed controller employs a simple gaussian radial-basis-function network as an uncertainty estimator. the proposed netw...

In this paper we evaluate the classiication accuracy of four statistical and three neural network classiiers for two image based pattern classiication problems. These are ngerprint classiication and optical character recognition (OCR) for isolated handprinted digits. The evaluation results reported here should be useful for designers of practical systems for these two important commercial appli...

An application of machine learning algorithms to the clustering and classification of chemical data concerning heroin seizures is presented. The data concerns the chemical constituents of heroin as given by a gas chromatography analysis. Following a preprocessing step, where the six initial constituents are reduced to only two significant features, the data are clustered in order to find natura...

This paper reports preliminary progress on a principled approach to modelling nonstationary phenomena using neural networks. We are concerned with both parameter and model order complexity estimation. The basic methodology assumes a Bayesian foundation. However t o allow the construction of pragmatic models, successive approximations have to be made l o permit computational tractibility. The lo...

In a deregulated power market, bidding decisions rely on good market clearing price prediction. One of the common forecasting methods is Gaussian radial basis function (GRBF) networks that approximate input–output relationships by building localized Gaussian functions (clusters). Currently, a cluster uses all the input factors. Certain input factors, however, may not be significant and should b...

In this paper a dynamically adaptive neural network architecture is investigated for robot behavior learning. Specifically, a so-called “Grow When Required” network (GWR) is used to dynamically cluster the sensor-motor training data for determining the centers of a radial basis function network (RBF), and then the RBF network is trained for acquiring and performing the required behaviors. We il...

In this work we want to analyse the behaviour of two classic Artificial Neural Network models respect to a data complexity measures. In particular, we consider a Radial Basis Function Network and a MultiLayer Perceptron. We examine the metrics of data complexity known as Measures of Separability of Classes over a wide range of data sets built from real data, and try to extract behaviour pattern...

| This paper is composed of two parts. The rst part surveys the literature regarding optimum nonlinear l-tering from the (continuous-time) stochastic analysis point of view, and the other part explores the impact of recent applications of neural networks (in a discrete-time context) to nonlinear ltering. In particular, the results obtained by using a regularized form of radial basis function (R...

Even though radial basis function networks are known to have good prediction accuracy in several domains, it is not known to decide a proper sample size like other data mining algorithms, so the task of deciding proper sample sizes for the networks tends to be arbitrary. As the size of samples grows, the improvement in error rates becomes better slowly. But we cannot use larger and larger sampl...