A parallel genetic algorithm for adaptive hardware and its application to ECG signal classification

This paper presents a parallel genetic algorithm (GA) called the cellular compact genetic algorithm (c-cGA) and its implementation for adaptive hardware. An adaptive hardware based on the c-cGA is proposed to automate real-time classification of ECG signals. The c-cGA not only provides a strong sear...

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Veröffentlicht in:	Neural computing & applications Jg. 22; H. 7-8; S. 1609 - 1626
Hauptverfasser:	Jewajinda, Yutana, Chongstitvatana, Prabhas
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Springer-Verlag 01.06.2013 Springer
Schlagworte:	Applied sciences Artificial Intelligence Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Computer science; control theory; systems Data Mining and Knowledge Discovery Exact sciences and technology Image Processing and Computer Vision Inference from stochastic processes; time series analysis Learning and adaptive systems Mathematics Multivariate analysis Original Article Parametric inference Probability and statistics Probability and Statistics in Computer Science Sciences and techniques of general use Statistics ECG signal classification Parallel genetic algorithm Adaptive hardware Parallel algorithm Neural computation Adaptive algorithm Adaptive system Field programmable gate array Long term variation Adaptive filter Neural network Real time Long term Implementation Learning Digital filter Genetic algorithm Classification Genetics Monitoring
ISSN:	0941-0643, 1433-3058
Online-Zugang:	Volltext
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