Dynamic System Identification Using A Type-2 Recurrent Fuzzy Neural Network

被引：0

作者：

Juang, Chia-Feng ^{[1
]}

Lin, Yang-Yin ^{[1
]}

Chung, I-Fang ^{[2
]}

机构：

[1] Natl Chung Hsing Univ, Dept Elect Engn, Taichung 402, Taiwan

[2] Natl Yang Ming Univ, Inst Biomed Informat, Taipei, Taiwan

来源：

ASCC: 2009 7TH ASIAN CONTROL CONFERENCE, VOLS 1-3 | 2009年

关键词：

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper proposes an Interval Type-2 Recurrent Fuzzy Neural Network (IT2RFNN) for dynamic system identification. The antecedent parts in each recurrent fuzzy rule in the IT2RFNN are interval type-2 fuzzy sets, and the consequent part is of the Takagi-Sugeno-Kang (TSK) type with interval weights. The recurrent structure in the T2RFNN enables it to handle dynamic system identification problems with a priori knowledge of system input and output delay numbers. A T2RFNN is constructed using concurrent structure and parameter learning. Simulations on dynamic system identification with clean and noisy outputs verify the performance of T2RFNN.

引用

页码：768 / 772

页数：5

共 50 条

[1] Nonlinear System Identification Using Type-2 Fuzzy Recurrent Wavelet Neural Network
Tafti, Bibi Elham Fallah
Khanesar, Mojtaba Ahmadieh
Teshnehlab, Mohammad
[J]. 2019 7TH IRANIAN JOINT CONGRESS ON FUZZY AND INTELLIGENT SYSTEMS (CFIS), 2019, : 75 - 78
[2] An Internal/Interconnection Recurrent Type-2 Fuzzy Neural Network (IRT2FNN) for Dynamic System Identification
Lin, Yang-Yin
Chang, Jyh-Yeong
Lin, Chin-Teng
[J]. 2010 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN AND CYBERNETICS (SMC 2010), 2010,
[3] Nonlinear dynamic systems identification using recurrent interval type-2 TSK fuzzy neural network - A novel structure
El-Nagar, Ahmad M.
[J]. ISA TRANSACTIONS, 2018, 72 : 205 - 217
[4] Dynamic system identification using a recurrent compensatory fuzzy neural network
Lee, Chi-Yung
Lin, Cheng-Jian
Chen, G-Hung
Chang, Chun-Lung
[J]. 2008, Institute of Control, Robotics and Systems (06)
[5] Dynamic system identification using a recurrent compensatory fuzzy neural network
Lee, Chi-Yung
Lin, Cheng-Jian
Chen, Cheng-Hung
Chang, Chun-Lung
[J]. INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS, 2008, 6 (05) : 755 - 766
[6] Evolving Type-2 Recurrent Fuzzy Neural Network
Pratama, Mahardhika
Lughofer, Edwin
Er, Meng Joo
Rahayu, Wenny
Dillon, Tharam
[J]. 2016 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2016, : 1841 - 1848
[7] A type-2 fuzzy wavelet neural network for system identification and control
Abiyev, Rahib H.
Kaynak, Okyay
Kayacan, Erdal
[J]. JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS, 2013, 350 (07): : 1658 - 1685
[8] A Recurrent Interval Type-2 Fuzzy Neural Network with Asymmetric Membership Functions for Nonlinear System Identification
Lee, Ching-Hung
Hu, Tzu-Wei
Lee, Chung-Ta
Lee, Yu-Chia
[J]. 2008 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS, VOLS 1-5, 2008, : 1498 - 1504
[9] A Recurrent Self-Evolving Interval Type-2 Fuzzy Neural Network for Dynamic System Processing
Juang, Chia-Feng
Huang, Ren-Bo
Lin, Yang-Yin
[J]. IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2009, 17 (05) : 1092 - 1105
[10] Systems identification using type-2 fuzzy neural network (Type-2 FNN) systems
Lee, CH
Lin, YC
Lai, WY
[J]. 2003 IEEE INTERNATIONAL SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE IN ROBOTICS AND AUTOMATION, VOLS I-III, PROCEEDINGS, 2003, : 1264 - 1269

← 1 2 3 4 5 →