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...

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...

In this paper we engineer an information mapping of transmission linkages across various European government bond markets. The research introduces a calibration methodology for the application of an optimizing radial basis function (RBF) artificial neural network (ANN). Utilizing a closed-form derivation of the regularization parameter, the Kajiji-4 RBF ANN is known to efficiently minimize the ...

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...

Several investigations around the world have been postulated that the infant cry can be utilized to asses the infant’s status and the use of Artificial Neural Networks (ANN) has been one of the recent alternatives to classify cry signals [4,9]. A Radial Basis Function (RBF) network is implemented for infant cry classification in order to find out relevant aspects concerned with the presence of ...

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...