首页|Memory Analysis for Memristors and Memristive Recurrent Neural Networks
Memory Analysis for Memristors and Memristive Recurrent Neural Networks
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
国家科技期刊平台
NETL
NSTL
万方数据
维普
Traditional recurrent neural networks are composed of capacitors,inductors,resistors,and operational amplifiers.Memristive neural networks are constructed by replacing resistors with memristors.This paper focuses on the memory analysis,i.e.the initial value computation,of memristors.Firstly,we present the memory analysis for a single memristor based on memristors' mathematical models with linear and nonlinear drift.Secondly,we present the memory analysis for two memristors in series and parallel.Thirdly,we point out the difference between traditional neural networks and those that are memristive.Based on the current and voltage relationship of memristors,we use mathematical analysis and SPICE simulations to demonstrate the validity of our methods.
Hubei Key Laboratory of Cascaded Hydropower Stations Operation and Control, Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002
Hubei Key Laboratory of Applied Mathematics(Hubei University), Wuhan 430074, China
Department of Medical Engineering, California Institute of Technology, Pasadena, California 91125 USA
School of Artificial Intelligence and Automation,Huazhong University of Science and Technology, and also with the Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China, Wuhan 430074, China
展开 >
work was supported by the National Natural Science Foundation of ChinaNational Key Research and Development Program of ChinaFoundation for Innovative Research Groups of Hubei Province of Chi
国家科技期刊平台
NSTL
万方数据
维普
