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

Date
2009-06-14
Authors
Schliebs, S
Defoin-Platel, M
Worner, S
Kasabov, N
Supervisor
Item type
Conference Contribution
Degree name
Journal Title
Journal ISSN
Volume Title
Publisher
IEEE
Abstract

The 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.

Description
Keywords
Biological system modeling , Neural networks
Source
Presentation at the International Joint Conference on Neural Networks (IJCNN '09), Atlanta, Georgia, USA, pp. 2833 - 2840
Rights statement
(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.