A spintronic memristive circuit on the optimized RBF-MLP neural network

doi:10.1088/1674-1056/ac6b1d

Abstract A radial basis function network (RBF) has excellent generalization ability and approximation accuracy when its parameters are set appropriately. However, when relying only on traditional methods, it is difficult to obtain optimal network parameters and construct a stable model as well. In view of this, a novel radial basis neural network (RBF-MLP) is proposed in this article. By connecting two networks to work cooperatively, the RBF's parameters can be adjusted adaptively by the structure of the multi-layer perceptron (MLP) to realize the effect of the backpropagation updating error. Furthermore, a genetic algorithm is used to optimize the network's hidden layer to confirm the optimal neurons (basis function) number automatically. In addition, a memristive circuit model is proposed to realize the neural network's operation based on the characteristics of spin memristors. It is verified that the network can adaptively construct a network model with outstanding robustness and can stably achieve 98.33% accuracy in the processing of the Modified National Institute of Standards and Technology (MNIST) dataset classification task. The experimental results show that the method has considerable application value.

Keywords: radial basis function network (RBF) genetic algorithm spintronic memristor memristive circuit

Received: 22 December 2021
Revised: 23 February 2022
Accepted manuscript online: 28 April 2022

PACS:	07.05.Mh	(Neural networks, fuzzy logic, artificial intelligence)
	84.35.+i	(Neural networks)
	85.35.-p	(Nanoelectronic devices)
	07.50.Ek	(Circuits and circuit components)

Corresponding Authors: Shukai Duan
E-mail: duansk@swu.edu.cn

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Yuan Ge(葛源), Jie Li(李杰), Wenwu Jiang(蒋文武), Lidan Wang(王丽丹), and Shukai Duan(段书凯) A spintronic memristive circuit on the optimized RBF-MLP neural network 2022 Chin. Phys. B 31 110702

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A spintronic memristive circuit on the optimized RBF-MLP neural network

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