General memristor with applications in multilayer neural networks. (July 2018)

• Record Type: Journal Article
• Authors: Wen, Shiping; Xie, Xudong; Yan, Zheng; Huang, Tingwen; Zeng, Zhigang
• Is Part Of: Neural networks. Volume 103(2018)
• Page Start: 142
• Page End: 149
• Publication Date: 2018-07
• Subjects: Memristor -- Window function -- Neural network; Neural computers -- Periodicals; Neural networks (Computer science) -- Periodicals; Neural networks (Neurobiology) -- Periodicals; Nervous System -- Periodicals; Ordinateurs neuronaux -- Périodiques; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux neuronaux (Neurobiologie) -- Périodiques; Neural computers; Neural networks (Computer science); Neural networks (Neurobiology); Periodicals; 006.32
• DOI: 10.1016/j.neunet.2018.03.015
• Languages: English
• ISSNs: 0893-6080

Abstract

Abstract: Memristor describes the relationship between charge and flux. Although several window functions for memristors based on the HP linear and nonlinear dopant drift models have been studied, most of them are inadequate to capture the full characteristics of memristors. To address this issue, this paper proposes a unified window function to describe a general memristor with restrictions of its parameters given. Compared with other window functions, the proposed function demonstrates high validity and accuracy. In order to make the simulation results have high consistency with the results of actual circuit, we apply the new window function to the simulation of a memristor-based multilayer neural network (MNN) circuit. The overall accuracy will vary with the change of control parameters in the window function. It implies that the proposed model can guide the design of actual memristor-based circuits.