%P 263-267
%C Kuala Lumpur
%T Bio-signal identification using simple growing RBF-network (OLACA)
%A V.S. Asirvadam
%A S.F. McLoone
%A R. Palaniappan
%D 2007
%R 10.1109/ICIAS.2007.4658387
%O cited By 2; Conference of 2007 International Conference on Intelligent and Advanced Systems, ICIAS 2007 ; Conference Date: 25 November 2007 Through 28 November 2007; Conference Code:74506
%J 2007 International Conference on Intelligent and Advanced Systems, ICIAS 2007
%L scholars149
%K Adaptive algorithms; Attitude control; Backpropagation; Cellular radio systems; Computational methods; Control theory; Electrocardiography; Electrochromic devices; Electroencephalography; Extended Kalman filters; Feedforward neural networks; Image segmentation; Learning algorithms; Neural networks; Planning; Resource allocation; Signal processing, Allocation algorithms; Computation loads; ECG signals; Fast sampling rates; Gaussian kernels; Learning rules; Minimal weights; Neural network adaptations; New algorithms; Original signals; Prediction errors; Propagation methods; Radial basis functions; Resource allocation networks; Second orders; Sequential learnings; Short time frames; Signal identifications; Significant reductions; Simulation results; Time series signals; Weight updates, Radial basis function networks
%X An enhanced online adaptive centre allocation algorithms (or resource allocation network (RAN)) using simple/ stochastic back-propagation method with minimal weight update variant are developed for Direct-Link Radial Basis Function (DRBF) networks. These algorithms are developed primarily for applications with fast sampling rate which demands significant reduction in computation load per iteration. The new algorithms are evaluated on a chaotic nonlinear biological based time series signals such as electroencephalographic (EEG) and electrocardiography (ECG). The EEG and ECG signals not only shows non-stationary behaviour but also large oscillation or changes. When the sample time is in milliseconds, both neural network adaptation and weight update must take place within the short time frame thus any learning rule must be computationally simple. The second order techniques, such as Extended Kalman Filter (EKF), need large amount of memory O(N2) and computationally intensive. The main goal of this paper is to develop a simple back-propagation based (SBP) resource allocation network (RAN), or also known as sequential learning technique using Radial Basis Function by incorporating Gaussian kernel, in order to identify (model) EEG and ECG signals. Simulation results show the modeled data show good representation of the original signals with less prediction error. ©2007 IEEE.