Journal
NEUROCOMPUTING
Volume 134, Issue -, Pages 269-279Publisher
ELSEVIER
DOI: 10.1016/j.neucom.2013.09.049
Keywords
Personalised modelling; Spatio-temporal pattern recognition; Spiking neural networks; Evolving connectionist systems; Stroke occurrence prediction
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Funding
- NZ Ministry of Business, Innovation and Enterprise for Strategic Research Alliance
- China Academy of Sciences Institute for Automation (CASIA)
- Knowledge Engineering and Discovery Research Institute (KEDRI)
- NISAN Institute of the Auckland University of Technology
- CASIA
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The paper presents a novel method and system for personalised (individualised) modelling of spatio/spectro-temporal data (SSTD) and prediction of events. A novel evolving spiking neural network reservoir system (eSNNr) is proposed for the purpose. The system consists of spike-time encoding module of continuous value input information into spike trains; a recurrent 3D SNNr; eSNN as an evolving output classifier. Such system is generated for every new individual, using existing data of similar individuals. Subject to proper training and parameter optimisation, the system is capable of accurate spatio-temporal pattern recognition (STPR) and of early prediction of individual events. The method and the system are generic, applicable to various SSTD and classification and prediction problems. As a case study, the method is applied for early prediction of occurrence of stroke on an individual basis. Preliminary experiments demonstrated a significant improvement in accuracy and time of event prediction when using the proposed method when compared with standard machine learning methods, such as MLR, SVM, and MLR Future development and applications are discussed. (C) 2014 Elsevier B.V. All rights reserved.
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