PMID- 32149234
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231113
IS  - 2470-1343 (Electronic)
IS  - 2470-1343 (Linking)
VI  - 5
IP  - 8
DP  - 2020 Mar 3
TI  - Prediction of the Actuation Property of Cu Ionic Polymer-Metal Composites Based 
      on Backpropagation Neural Networks.
PG  - 4067-4074
LID - 10.1021/acsomega.9b03725 [doi]
AB  - Ionic polymer-metal composite (IPMC) actuators are one of the most prominent 
      electroactive polymers with expected widespread use in the future. The IPMC bends 
      in response to a small applied electric field as a result of the mobility of 
      cations in the polymer network. This paper proposes a Levenberg-Marquardt 
      algorithm backpropagation neural network (LMA-BPNN) prediction model applicable 
      for Cu/Nafion-based ionic polymer-metal composites to predict the actuation 
      property. The proposed approach takes the dimension ratio (DR) and stimulation 
      voltage as the input layer, displacement and blocking force as the output layer, 
      and trains the LMA-BPNN with the experimental data so as to obtain a mapping 
      relationship between the input and the output and obtain the predicted values of 
      displacement and blocking force. An IPMC actuating system is set up to generate a 
      collection of the IPMC actuating data. Based on the input/output training data, 
      the most suitable structure was found out for the BPNN model to represent the 
      IPMC actuation behavior. After training and verification, a 2-9-3-1 BPNN 
      structure for displacement and a 2-9-4-1 BPNN structure for blocking force 
      indicate that the structure can provide a good reference value for the IPMC. The 
      results showed that the BPNN model based on the LMA could predict the 
      displacement and blocking force of the IPMC. Therefore, this model can become an 
      effective solution for IPMC control applications.
CI  - Copyright (c) 2020 American Chemical Society.
FAU - Yang, Liang
AU  - Yang L
AD  - School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, China.
FAU - Zhang, Dongsheng
AU  - Zhang D
AD  - School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, China.
FAU - Zhang, Xining
AU  - Zhang X
AD  - School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, China.
FAU - Tian, Aifen
AU  - Tian A
AD  - School of Materials Science and Engineering, Xi'an University of Science and 
      Technology, Xi'an 710054, China.
LA  - eng
PT  - Journal Article
DEP - 20200219
PL  - United States
TA  - ACS Omega
JT  - ACS omega
JID - 101691658
PMC - PMC7057690
COIS- The authors declare no competing financial interest.
EDAT- 2020/03/10 06:00
MHDA- 2020/03/10 06:01
PMCR- 2020/02/19
CRDT- 2020/03/10 06:00
PHST- 2019/11/03 00:00 [received]
PHST- 2020/02/04 00:00 [accepted]
PHST- 2020/03/10 06:00 [entrez]
PHST- 2020/03/10 06:00 [pubmed]
PHST- 2020/03/10 06:01 [medline]
PHST- 2020/02/19 00:00 [pmc-release]
AID - 10.1021/acsomega.9b03725 [doi]
PST - epublish
SO  - ACS Omega. 2020 Feb 19;5(8):4067-4074. doi: 10.1021/acsomega.9b03725. eCollection 
      2020 Mar 3.