非线性系统
记忆电阻器
计算机科学
非线性系统辨识
油藏计算
鉴定(生物学)
人工神经网络
系统标识
光学(聚焦)
系列(地层学)
领域(数学)
能源消耗
控制理论(社会学)
控制工程
人工智能
数据建模
循环神经网络
工程类
数学
电子工程
物理
植物
电气工程
光学
控制(管理)
量子力学
数据库
纯数学
生物
古生物学
作者
Hongbo Liu,Shukai Duan,Wenwu Jiang,Jie Li,Lidan Wang
标识
DOI:10.1109/icet55676.2022.9824316
摘要
Nonlinear systems have attracted a lot of attention because of their widespread existence in nature and life. Among them, the modeling and prediction of nonlinear systems is the focus of the research field of nonlinear systems. Although the traditional neural network has achieved good results, it is not conducive to being applied to practical problems due to the unsatisfactory training speed and large energy consumption. In this paper, considering the nonlinear characteristics of the memristor and the fast training speed of reservoir computing, we combine memristor and reservoir computing. Lorenz time series prediction and second-order nonlinear system modeling tasks are demonstrated. The results show that our model performs well in nonlinear time series prediction and nonlinear system model identification, the feasibility of the method is demonstrated. This is of great significance to the study of nonlinear systems and can be effectively applied to the analysis of nonlinear systems.
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