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

Schliebs, S
Defoin-Platel, M
Worner, S
Kasabov, N
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Conference Contribution
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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.

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