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- W2560012912 abstract "In this paper, differential evolution DE has been utilised to solve the problem of tuning the parameters of evolving spiking neural network ESNN manually. As ESNN is sensitive to its parameters as other models, optimal integration of parameters leads to better classification accuracy. A hybrid differential evolution for parameter tuning of evolving spiking neural network DEPT-ESNN is presented for parameter optimisation for determining the optimal number of evolving spiking neural network ESNN parameters: modulation factor Mod, similarity factor Sim and threshold factor C. The best values of parameters are adaptively selected by differential evolution DE to avoid selecting suitable values for a particular problem by trial-and-error approach. Several standard datasets from UCI machine learning are used for evaluating the performance of this hybrid model. It has been found that the classification accuracy and other performance measures can be increased by using hybrid method with differential evolution DEPT_ESNN." @default.
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- W2560012912 date "2017-01-01" @default.
- W2560012912 modified "2023-10-02" @default.
- W2560012912 title "A hybrid differential evolution algorithm for parameter tuning of evolving spiking neural network" @default.
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- W2560012912 doi "https://doi.org/10.1504/ijcvr.2017.081231" @default.
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