Stabilization Control of an Inverted Pendulum by Complex-valued Neuro-Fuzzy Learning Algorithm

被引：0

作者：

Arai, Jin ^{[1
]}

Hata, Ryusuke ^{[1
]}

Murase, Kazuyuki ^{[1
]}

机构：

[1] Univ Fukui, Grad Sch Engn, Fukui, Japan

来源：

2014 JOINT 7TH INTERNATIONAL CONFERENCE ON SOFT COMPUTING AND INTELLIGENT SYSTEMS (SCIS) AND 15TH INTERNATIONAL SYMPOSIUM ON ADVANCED INTELLIGENT SYSTEMS (ISIS) | 2014年

关键词：

Neuro-fuzzy; Fuzzy; Neural networks; Complex-valued neural networks; Inverted pendulum;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

As an automatic generation method of the fuzzy rules, the complex-valued neuro-fuzzy (CVNF) learning algorithm has been suggested. That is a method expanded the conventional neuro-fuzzy (NF) learning algorithm into the complex domain. The purpose of this study is to verify the effectiveness of the CVNF by performing the stabilization control of an inverted pendulum using CVNF, and comparing it with NF. As a result, the control of the inverted pendulum was possible in CVNF with higher precision than in NF.

引用

页码：649 / 654

页数：6

共 50 条

[21] Stabilization of inverted pendulum by the genetic algorithm
Omatu, S
Deris, S
[J]. 1996 IEEE INTERNATIONAL CONFERENCE ON EVOLUTIONARY COMPUTATION (ICEC '96), PROCEEDINGS OF, 1996, : 700 - 705
[22] Adaptive Control of Nonlinear System Using Neuro-Fuzzy Learning by PSO Algorithm
Turki, Mourad
Bouzaida, Sana
Sakly, Anis
M'Sahli, Faouzi
[J]. 2012 16TH IEEE MEDITERRANEAN ELECTROTECHNICAL CONFERENCE (MELECON), 2012, : 519 - 523
[23] Stabilization of inverted pendulum by the genetic algorithm
Omatu, S
Deris, S
[J]. ETFA '96 - 1996 IEEE CONFERENCE ON EMERGING TECHNOLOGIES AND FACTORY AUTOMATION, PROCEEDINGS, VOLS 1 AND 2, 1996, : 282 - 287
[24] A decision-making approach on control techniques for an inverted pendulum based on, neuro-fuzzy, indirect adaptive and PID controllers
de la Cruz-alejo, Jesus
Beatriz-Cuellar, Hugo
Guillermo, I. C. Alcocer
Ortega, Agustin Mora
[J]. DISCOVER APPLIED SCIENCES, 2024, 6 (05)
[25] Decentralized decoupled sliding-mode control for two-dimensional inverted pendulum using neuro-fuzzy modeling
Farrokhi, M
Ghanbari, A
[J]. ADVANCED FUZZY-NEURAL CONTROL 2001, 2002, : 143 - 148
[26] A new learning algorithm for a forecasting neuro-fuzzy network
Otto, P
Bodyanskiy, Y
Kolodyazhniy, V
[J]. INTEGRATED COMPUTER-AIDED ENGINEERING, 2003, 10 (04) : 399 - 409
[27] Rule extraction using a neuro-fuzzy learning algorithm
Liu, ZQ
Zhang, YJ
[J]. PROCEEDINGS OF THE 12TH IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS, VOLS 1 AND 2, 2003, : 1401 - 1405
[28] Robust Learning Algorithm for Networks of Neuro-Fuzzy Units
Bodyanskiy, Yevgeniy
Popov, Sergiy
Titov, Mykola
[J]. INNOVATIONS AND ADVANCES IN COMPUTER SCIENCES AND ENGINEERING, 2010, : 343 - +
[29] Friction compensation of double inverted pendulum on a cart using locally linear neuro-fuzzy model
Atabak Nejadfard
M. J. Yazdanpanah
Iraj Hassanzadeh
[J]. Neural Computing and Applications, 2013, 22 : 337 - 347
[30] A Comparative Study of the Joint Neuro-Fuzzy Friction Models for a Triple Link Rotary Inverted Pendulum
Ben Hazem, Zied
Fotuhi, Mohammad Javad
Bingul, Zafer
[J]. IEEE ACCESS, 2020, 8 : 49066 - 49078

← 1 2 3 4 5 →