A multi-objective genetic type-2 fuzzy extreme learning system for the identification of nonlinear dynamic systems

Multi Objective

Adaptive Nonlinear PID Control Using Output Recurrent Broad Learning System for Discrete-Time Nonlinear Dynamic Systems

10.1109/icsse52999.2021.9538497 ◽

2021 ◽

Author(s):

Guo-Shun Hung ◽

Ching-Chih Tsai

Keyword(s):

Discrete Time ◽

Dynamic Systems ◽

Pid Control ◽

Nonlinear Dynamic ◽

Learning System ◽

Nonlinear Pid Control

Modeling of unstructured uncertainties and robust controlling of nonlinear dynamic systems based on type-2 fuzzy basis function networks

Engineering Applications of Artificial Intelligence ◽

10.1016/j.engappai.2016.03.010 ◽

2016 ◽

Vol 53 ◽

pp. 74-85 ◽

Cited By ~ 8

Author(s):

Phuong D. Ngo ◽

Yung C. Shin

Keyword(s):

Dynamic Systems ◽

Basis Function ◽

Nonlinear Dynamic ◽

Broad Learning System for Control of Nonlinear Dynamic Systems

2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC) ◽

10.1109/smc.2018.00383 ◽

2018 ◽

Cited By ~ 5

Author(s):

Shuang Feng ◽

C. L. Philip Chen

Keyword(s):

Dynamic Systems ◽

Nonlinear Dynamic ◽

Learning System ◽

Type-2 fuzzy instrumental variable algorithm for evolving neural-fuzzy modeling of nonlinear dynamic systems in noisy environment

Engineering Applications of Artificial Intelligence ◽

10.1016/j.engappai.2021.104620 ◽

2022 ◽

Vol 109 ◽

pp. 104620

Author(s):

Anderson Pablo Freitas Evangelista ◽

Ginalber Luiz de Oliveira Serra

Keyword(s):

Dynamic Systems ◽

Nonlinear Dynamic ◽

Instrumental Variable ◽

Fuzzy Modeling ◽

Noisy Environment ◽

Neural Fuzzy

Identification of Nonlinear Dynamic Systems Using Type-2 Fuzzy Neural Networks—A Novel Learning Algorithm and a Comparative Study

IEEE Transactions on Industrial Electronics ◽

10.1109/tie.2014.2345353 ◽

2015 ◽

Vol 62 (3) ◽

pp. 1716-1724 ◽

Cited By ~ 55

Author(s):

Erkan Kayacan ◽

Erdal Kayacan ◽

Mojtaba Ahmadieh Khanesar

Keyword(s):

Neural Networks ◽

Comparative Study ◽

Dynamic Systems ◽

Nonlinear Dynamic ◽

Learning Algorithm ◽

Fuzzy Neural Networks ◽

Fuzzy Neural

Recovery of State Vector of Nonlinear Dynamic Systems

Journal of Automation and Information Sciences ◽

10.1615/jautomatinfscien.v39.i9.10 ◽

2007 ◽

Vol 39 (9) ◽

pp. 1-15

Author(s):

Vsevold M. Kuntsevich

Keyword(s):

Dynamic Systems ◽

Nonlinear Dynamic ◽

State Vector ◽

Nonparametric Models for Nonlinear Dynamic Systems: A Case Study

10.23919/acc.1990.4790816 ◽

1990 ◽

Author(s):

Alexander A. Georgiev

Keyword(s):

Dynamic Systems ◽

Nonlinear Dynamic ◽

Nonparametric Models

Change detection in partially observed nonlinear dynamic systems with unknown change parameters

Proceedings of the 2004 American Control Conference ◽

10.23919/acc.2004.1384709 ◽

2004 ◽

Cited By ~ 2

Author(s):

N. Vaswani

Keyword(s):

Change Detection ◽

Dynamic Systems ◽

Nonlinear Dynamic ◽

Partially Observed

Model Checking Control Software for Nonlinear Dynamic Systems

AIAA Guidance, Navigation and Control Conference and Exhibit ◽

10.2514/6.2008-6800 ◽

2008 ◽

Author(s):

James Kapinski ◽

Alexandre Donze ◽

Flavio Lerda ◽

Hitashyam Maka ◽

Edmund Clarke ◽

...

Keyword(s):

Model Checking ◽

Dynamic Systems ◽

Nonlinear Dynamic ◽

Control Software

Emulation of Ensembles of Nonlinear Dynamic Systems in Ultrawideband Active Wireless Direct Chaotic Networks

Физические основы приборостроения ◽

10.25210/jfop-1803-038049 ◽

2018 ◽

Vol 7 (3) ◽

pp. 38-49

Author(s):

Yu.V. Andreyev ◽

◽

M.Yu. Gerasimov ◽

A.S. Dmitriev ◽

R.Yu. Yemelyanov ◽

...

Keyword(s):

Dynamic Systems ◽

Nonlinear Dynamic ◽