Approximation to Nonlinear Discrete-Time Systems by Recurrent Neural Networks

被引：0
|
作者：
Li, Fengjun [1 ]
机构：
[1] Ningxia Univ, Sch Math & Comp Sci, Yinchuan 750021, Peoples R China
来源：
SIXTH INTERNATIONAL SYMPOSIUM ON NEURAL NETWORKS (ISNN 2009) | 2009年 / 56卷
关键词：
Approximation; nonlinear discrete-time system; recurrent neural networks; UNIVERSAL APPROXIMATORS; DYNAMICAL-SYSTEMS;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Neural networks are widely used to approximate nonlinear functions. In order to study its approximation capability, a approximating approach for nonlinear discrete-time systems is presented by using the concept of the time-variant recurrent neural networks (RNNs) and the theory of two-dimensional systems. Both theory and simulations results show that the derived mathematical model of RNNs can approximate the nonlinear dynamical systems to any degree of accuracy.
引用
收藏
页码：527 / 534
页数：8
相关论文
共 50 条
  • [21] Adaptive sliding-mode observer for second order discrete-time MIMO nonlinear systems based on recurrent neural-networks
    Salgado, Ivan
    Ahmed, Hafiz
    Camacho, Oscar
    Chairez, Isaac
    INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS, 2019, 10 (10) : 2851 - 2866
  • [22] Nonlinear discrete-time observers with Physics-Informed Neural Networks
    Alvarez, Hector Vargas
    Fabiani, Gianluca
    Kazantzis, Nikolaos
    Kevrekidis, Ioannis G.
    Siettos, Constantinos
    CHAOS SOLITONS & FRACTALS, 2024, 186
  • [23] Using time-discrete recurrent neural networks in nonlinear control
    Kolb, T
    Ilg, W
    Wille, J
    IEEE WORLD CONGRESS ON COMPUTATIONAL INTELLIGENCE, 1998, : 1367 - 1371
  • [24] Exponential Lagrange stability for impulses in discrete-time delayed recurrent neural networks
    Jiang, Wenlin
    Li, Liangliang
    Tu, Zhengwen
    Feng, Yuming
    INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE, 2019, 50 (01) : 50 - 59
  • [25] A Special Criteria to Globally Exponentially Stability for Discrete-time Recurrent Neural Networks
    Yuan, Jimin
    Wu, Weigen
    Yin, Xin
    ADVANCED MATERIALS SCIENCE AND TECHNOLOGY, PTS 1-2, 2011, 181-182 : 293 - +
  • [26] Robust passivity analysis for discrete-time recurrent neural networks with mixed delays
    Huang, Chuan-Kuei
    Shu, Yu-Jeng
    Chang, Koan-Yuh
    Shou, Ho-Nien
    Lu, Chien-Yu
    INTERNATIONAL JOURNAL OF ELECTRONICS, 2015, 102 (02) : 216 - 232
  • [27] Discrete-time neural control without projection for a class of nonlinear systems
    Yu, Wen
    Li, Xiaoou
    2010 IEEE INTERNATIONAL SYMPOSIUM ON INTELLIGENT CONTROL, 2010, : 1504 - 1508
  • [28] Approximation of dynamical time-variant systems by continuous-time recurrent neural networks
    Li, XD
    Ho, JKL
    Chow, TWS
    IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS, 2005, 52 (10) : 656 - 660
  • [29] Stability of discrete time recurrent neural networks and nonlinear optimization problems
    Singh, Jayant
    Barabanov, Nikita
    NEURAL NETWORKS, 2016, 74 : 58 - 72
  • [30] Direct adaptive control for a class of discrete-time unknown nonaffine nonlinear systems using neural networks
    Yang, Xiong
    Liu, Derong
    Wei, Qinglai
    Wang, Ding
    INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, 2015, 25 (12) : 1844 - 1861
12345