| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 495862 | Applied Soft Computing | 2014 | 7 Pages |
•Evolving system based on self-learning fuzzy spiking neural network is proposed.•The adaptive wavelet activation-membership functions usage is extended.•Complex clusters detection capability is achieved via evolving architecture.
The paper introduces several modifications to self-learning fuzzy spiking neural network that is used as a base for evolving system design. The adaptive wavelet activation-membership functions are utilized to improve and generalize receptive neuron activation functions and the temporal Hebbian learning algorithm. The proposed evolving spiking wavelet-neuro-fuzzy self-learning system retains native features of spiking neurons and reveals evolving systems’ capabilities in detecting overlapping clusters of irregular form.
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