A study is presented to compare the performance of multilayer perceptrons, radial basis function networks, and probabilistic neural networks for classification. In many classification problems, probabilistic neural networks have outperformed other neural classifiers. Unfortunately, with this kind of networks, the number of required operations to classify one pattern directly depends on the numb...

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...

The typical system identi cation procedure requires powerful and versatile software means. In this paper we describe and exemplify the use of a prototype identi cation software tool, applicable for the rather broad class of multi input single output model structures with regressors that are formed by delayed inand outputs. Interesting special instances of this model structure category include, ...

A Bayesian-based methodology is presented which leads to a data analysis system based around a committee of radial-basis function (RBF) networks. We show that this approach enables estimatation of the uncertainty associated with system outputs. Systems with diiering numbers of internal degrees of freedom (weights) may hence be compared using training data only. Feedforward neural networks have ...

A novel two stage Improved Radial Basis Function (IRBF) neural network for the damage identification of a multimember structure in the frequency domain is presented. The improvement of the proposed IRBF network is carried out in two stages. Conventional RBF network is used in the first stage for preliminary damage prediction and in the second stage reduced search space moving technique is used ...

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...

Neural networks are intended to be used in future nanoelectronic systems since neural architectures seem to be robust against malfunctioning elements and noise in their weights. In this paper we analyze the fault-tolerance of Radial Basis Function networks to StuckAt-Faults at the trained weights and at the output of neurons. Moreover, we determine upper bounds on the mean square error arising ...

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...

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...

Support Vector Machines (SVMs) have been applied to solve the classification of volatile organic compounds (VOC) data in some recent studies. SVMs provide good generalization performance in detection and classification of VOC data. However, in many applications involving VOC data, it is not unusual for additional data, which may include new classes, to become available over time, which then req...