PMID- 38198261
OWN - NLM
STAT- Publisher
LR  - 20240110
IS  - 2162-2388 (Electronic)
IS  - 2162-237X (Linking)
VI  - PP
DP  - 2024 Jan 10
TI  - Self-Constructing Chebyshev Fuzzy Neural Complementary Sliding Mode Control and 
      Its Application.
LID - 10.1109/TNNLS.2023.3347767 [doi]
AB  - In this article, a complementary sliding mode (CSM) controller using a 
      self-constructing Chebyshev fuzzy recurrent neural network (SCCFRNN) is proposed 
      for harmonic suppression control of an active power filter (APF). The SCCFRNN 
      whose structure can be automatically learned through the designed structure 
      self-learning algorithm is introduced to approximate the unknown nonlinear term 
      in the APF dynamic model, so as to improve modeling accuracy and reduce the 
      burden of CSM control (CSMC). The SCCFRNN combines the advantages of a fuzzy 
      neural network (FNN), recurrent neural network (RNN), and Chebyshev neural 
      network (CNN), and all parameters can be adjusted according to the designed 
      adaptive laws. Eventually, through detailed simulation, hardware experiments, and 
      fair comparison, the feasibility and superiority of the proposed control 
      algorithm were verified.
FAU - Fei, Juntao
AU  - Fei J
FAU - Zhang, Lei
AU  - Zhang L
FAU - Fang, Yunmei
AU  - Fang Y
LA  - eng
PT  - Journal Article
DEP - 20240110
PL  - United States
TA  - IEEE Trans Neural Netw Learn Syst
JT  - IEEE transactions on neural networks and learning systems
JID - 101616214
SB  - IM
EDAT- 2024/01/10 18:42
MHDA- 2024/01/10 18:42
CRDT- 2024/01/10 12:34
PHST- 2024/01/10 18:42 [medline]
PHST- 2024/01/10 18:42 [pubmed]
PHST- 2024/01/10 12:34 [entrez]
AID - 10.1109/TNNLS.2023.3347767 [doi]
PST - aheadofprint
SO  - IEEE Trans Neural Netw Learn Syst. 2024 Jan 10;PP. doi: 
      10.1109/TNNLS.2023.3347767.