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Bio-Signal Identification using Simple Growing RBF-Network (OLACA)

Asirvadam, Vijanth S., McLoone, Sean F., Palaniappan, Ramaswamy (2008) Bio-Signal Identification using Simple Growing RBF-Network (OLACA). In: Icias 2007: International Conference on Intelligent & Advanced Systems, Proceedings. . pp. 263-267. IEEE ISBN 978-1-4244-1355-3. (doi:10.1109/ICIAS.2007.4658387) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL
http://dx.doi.org/10.1109/ICIAS.2007.4658387

Abstract

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.

Item Type: Conference or workshop item (Proceeding)
DOI/Identification number: 10.1109/ICIAS.2007.4658387
Additional information: Unmapped bibliographic data: ST - Bio-Signal Identification using Simple Growing RBF-Network (OLACA) [Field not mapped to EPrints] AN - WOS:000260249900051 [Field not mapped to EPrints]
Divisions: Faculties > Sciences > School of Computing
Faculties > Sciences > School of Computing > Data Science
Depositing User: Palaniappan Ramaswamy
Date Deposited: 15 Dec 2018 11:41 UTC
Last Modified: 29 May 2019 14:29 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/48204 (The current URI for this page, for reference purposes)
Palaniappan, Ramaswamy: https://orcid.org/0000-0001-5296-8396
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