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- W2186548499 abstract "Spiking neural network (SNN) is considered as the third generation of artificial neural networks. Although there are many models of SNN, Evolving Spiking Neural Network (ESNN) is widely used in many recent research works. Evolutionary algorithms (EAs) have been used to optimize the ESNN, which has been used to solve the learning problems. In this paper, the differential evolution (DE) - as one of EAs- is used to determine the optimal number of pre-synaptic neurons for a given dataset which is required before the ESNN structure. This number of pre-synaptic neurons is deemed the complexity of ESNN. Five standard datasets from the UCI machine learning are used for evaluating the performance of this hybrid model. The experimental results have proved that the hybrid of Differential Evolution with Evolving Spiking Neural Network (DE-ESNN) gives promising results in terms of accuracy and complexity." @default.
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- W2186548499 date "2014-01-01" @default.
- W2186548499 modified "2023-09-23" @default.
- W2186548499 title "A Novel hybrid algorithm of Differential evolution with Evolving Spiking Neural Network for pre-synaptic neurons Optimization" @default.
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