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        Memristor-based neural network circuit with weighted sum simultaneous perturbation training and its applications

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        Neurocomputing_MNN_Circuit.pdf (PDF, 747Kb)(embargoed until 20/08/2022)
        Author
        Xu, Cong
        Wang, Chunhua
        Sun, Yichuang
        Hong , Qinghui
        Deng, Quanli
        Chen, Haowen
        Attention
        2299/25036
        Abstract
        In this work, a full circuit of memristor-based neural network with weighted sum simultaneous perturbation training is proposed. Firstly, a synaptic circuit is designed by using a pair of memristors, which can represent negative, zero, and positive synaptic weights. Secondly, a full circuit of the neural network is designed, with all operations being completed on the circuit without any computer aid. The neural network is trained with the weighted sum simultaneous perturbation algorithm. The algorithm does not involve complex derivative calculation and error back propagation, and it only applies perturbations to weighted sum, so the circuit implementation is more simple. Finally, application simulations of the proposed neural network circuit are performed via PSpice. The results of simulation indicate that the memristor-based neural network is practical and effective.
        Publication date
        2021-08-20
        Published in
        Neurocomputing
        Published version
        https://doi.org/10.1016/j.neucom.2021.08.072
        Other links
        http://hdl.handle.net/2299/25036
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