Supervised Artificial Neural Networks (ANN) are information processing systems that adapt their functionality as a result of exposure to input-output examples. To this end, there exist generic procedures and techniques, known as learning rules. The most widely used in the neural network context rely in derivative information, and are typically associated with the Multilayer Perceptron (MLP). Ot...

Ratio pulse arithmetic represents a signal with a random pulse stream and its value is defined as the ratio of ones and zeroes in its random pulse stream. We propose circuits for the exponential function and subtraction, and explain how to implement a radial basis function(RBF) network. We applied the RBF network to a classifier example. Simulation results show that its performance is comparabl...

This paper proposes a novel algorithm based on minimizing mutual information for a special case of nonlinear blind source separation: postnonlinear blind source separation. A network composed of a set of radial basis function (RBF) networks, a set of multilayer perceptron and a linear network is used as a demixing system to separate sources in post-nonlinear mixtures. The experimental results s...

This paper investigates the potential application of artificial neural networks in permanent deformation parameter identification for rockfill dams. Two kinds of neural network models, multilayer feedforward network BP and radial basis function RBF networks, are adopted to identify the parameters of seismic permanent deformation for Zipingpu Dam in China. The dynamic analysis is carried out by ...

The Hartley transform, a real-valued alternative to the complex Fourier transform, is presented as an efficient tool for the analysis and simulation of earthquake accelerograms. This paper is introduced a novel method based on discrete Hartley transform (DHT) and radial basis function (RBF) neural network for generation of artificial earthquake accelerograms from specific target spectrums. Acce...

In order to robustly execute a task under environmental uncertainty, a robot needs to be able to reactively adapt to changes arising in its environment. The environment changes are usually reflected in deviation in sensory traces from nominal. These deviations in sensory traces can be used to drive the plan adaptation, and for this purpose, a feedback model is required. The feedback model maps ...

We propose a self-organising hierarchical Radial Basis Function (RBF) network for functional modelling of large amounts of scattered unstructured point data. The network employs an error-driven active learning algorithm and a multi-layer architecture, allowing progressive bottom-up reinforcement of local features in subdivisions of error clusters. For each RBF subnet, neurons can be inserted, r...

Power quality monitors (PQM) are required to be installed in a power supply network in order to assess power quality (PQ) disturbances such as voltage sags. However, with few PQMs installation, it is difficult to pinpoint the exact location of voltage sag. This paper proposes a new method for identifying the voltage sag source location by using the artificial neural network (ANN). Radial basis ...

A new approach, consisting of using radial basis function networks to obtain the long-range part of diatomic potential energy functions from simulated second virial coefficients, is presented. From these simulated data the artiÐcial neural network was able not only to learn but also to predict properties for systems that were not considered during the training process. Fifteen di†erent diatomic...

This paper presents mesh-free procedures for solving linear differential equations (ODEs and elliptic PDEs) based on multiquadric (MQ) radial basis function networks (RBFNs). Based on our study of approximation of function and its derivatives using RBFNs that was reported in an earlier paper (Mai-Duy, N. & Tran-Cong, T. (1999). Approximation of function and its derivatives using radial basis fu...