Quantum-inspired feature and parameter optimization of evolving spiking neural networks with a case study from ecological modelling

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aut.researcherSchliebs, Stefan
dc.contributor.authorSchliebs, S
dc.contributor.authorDefoin-Platel, M
dc.contributor.authorWorner, S
dc.contributor.authorKasabov, N
dc.date.accessioned2011-08-04T00:30:21Z
dc.date.available2011-08-04T00:30:21Z
dc.date.copyright2009-06-14
dc.date.issued2009-06-14
dc.description.abstractThe paper introduces a framework and implementation of an integrated connectionist system, where the features and the parameters of an evolving spiking neural network are optimised together using a quantum representation of the features and a quantum inspired evolutionary algorithm for optimisation. The proposed model is applied on ecological data modeling problem demonstrating a significantly better classification accuracy than traditional neural network approaches and a more appropriate feature subset selected from a larger initial number of features. Results are compared to a naive Bayesian classifier.
dc.identifier.citationPresentation at the International Joint Conference on Neural Networks (IJCNN '09), Atlanta, Georgia, USA, pp. 2833 - 2840
dc.identifier.doi10.1109/IJCNN.2009.5179049
dc.identifier.isbn978-1-4244-3548-7 (print)
dc.identifier.issn1098-7576
dc.identifier.urihttps://hdl.handle.net/10292/1561
dc.publisherIEEE
dc.relation.urihttp://dx.doi.org/10.1109/IJCNN.2009.5179049
dc.rights(c) 2009 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
dc.rights.accessrightsOpenAccess
dc.subjectBiological system modeling
dc.subjectNeural networks
dc.titleQuantum-inspired feature and parameter optimization of evolving spiking neural networks with a case study from ecological modelling
dc.typeConference Contribution
pubs.organisational-data/AUT
pubs.organisational-data/AUT/Design & Creative Technologies
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