Dynamic Power Management (DPM) is a technique to reduce power consumption of electronic system by selectively shutting down idle components. In this article we try to introduce back propagation network and radial basis network into the research of the systemlevel power management policies. We proposed two PM policiesBack propagation Power Management (BPPM) and Radial Basis Function Power Manage...

Normalisation of the basis function activations in a radial basis function (RBF) network is a common way of achieving the partition of unity often desired for modelling applications. It results in the basis functions covering the whole of the input space to the same degree. However, normalisa-tion of the basis functions can lead to other eeects which are sometimes less desireable for modelling ...

A Radial Basis Function Network (RBFN) classifier for hard fault location in CMOS analogue circuit is presented. The network is trained by means of a fault dictionary containing the faulty circuit response, which is obtained by simulating the supply current dynamic response.

Machine based human action recognition has become very popular in the last decade. Automatic unattended surveillance systems, interactive video games, machine learning and robotics are only few of the areas that involve human action recognition. This paper examines the capability of a known transform, the so-called Trace, for human action recognition and proposes two new feature extraction meth...

This paper shows a method to diagnose potential mispronunciations in second language learning by studying the characteristics of the speech produced by a group of native speakers and the speech produced by various non-native groups of speakers from diverse language backgrounds. The method compares the native auditory perception and the non-native spectral representation on the phoneme level usi...

We present probabilistic models which are suitable for class conditional density estimation and can be regarded as shared kernel models where sharing means that each kernel may contribute to the estimation of the conditional densities of an classes. We first propose a model that constitutes an adaptation of the classical radial basis function (RBF) network (with full sharing of kernels among cl...

This paper presented the Minimal Radial Basis Function Network (MRBFN) approach busbar protection. The Optical Current Transducer (OCT) is used to solve the magnetic saturation so as to improve the reliability of the system. Performance of this model is compared with Feed Forward Back Propagation Neural Network (FFBP). The proposed model is more accurate in prediction with few numbers of hidden...

In regression problems, making accurate predictions is often the primary goal. Also, relevance of inputs in the prediction of an output would be valuable information in many cases. A sequential input selection algorithm for Radial basis function (SISAL-RBF) networks is presented to analyze importances of the inputs. The ranking of inputs is based on values, which are evaluated from the partial ...

This paper presents an alternating minimization algorithm used to train radial basis function networks. The algorithm is a modiication of an interior point method used in solving primal linear programs. The resulting algorithm is shown to have a convergence rate on the order of p nL iterations where n is a measure of the network size and L is a measure of the resulting solution's accuracy.