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

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4 Author(s)
Schliebs, S. ; Knowledge Eng. & Discovery Res. Inst. (KEDRI), Auckland Univ. of Technol., Auckland, New Zealand ; Platel, M.D. ; Worner, S. ; Kasabov, N.

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.

Published in:

Neural Networks, 2009. IJCNN 2009. International Joint Conference on

Date of Conference:

14-19 June 2009

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