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Neural Computing and Applications
Published in: Neural Computing and Applications 8/2019

09-01-2018 | Original Article

SpikeCD: a parameter-insensitive spiking neural network with clustering degeneracy strategy

Authors: Peng Lin, Sheng Chang, Hao Wang, Qijun Huang, Jin He

Published in: Neural Computing and Applications | Issue 8/2019

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Abstract

A clustering degeneracy algorithm, called SpikeCD, with spiking RBF neurons for classification is proposed in this paper. Unlike traditional spiking RBF networks where their performance severely relies on the time-costing process of parameter optimization, SpikeCD uses a clustering degeneracy strategy to adjust the number and centers of spiking RBF neurons, which is insensitive to parameters. A supervised learning is followed to improve network’s classification ability. Its performance is demonstrated on several benchmark datasets from the UCI Machine Learning Repository and image datasets. The results show SpikeCD can achieve good classification accuracy with simple structure. Moreover, the variation of parameters has a little effect on it. We hope this algorithm can be a new inspiration for improving the robustness of evolving spiking neural networks and other machine learning methods.
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Metadata
Title
SpikeCD: a parameter-insensitive spiking neural network with clustering degeneracy strategy
Authors
Peng Lin
Sheng Chang
Hao Wang
Qijun Huang
Jin He
Publication date
09-01-2018
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 8/2019
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-017-3336-6

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