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Research on Activated Carbon Supercapacitors Electrochemical Properties Based on Improved PSO-BP Neural Network

Xiaoyi Liang1, Zhen Yang1,2, Xingsheng Gu3, Licheng Ling1
State Key Laboratory of Chemical Engineering, East China University of Science and Technology; Key Laboratory for Special functional Polymer Materials and Their Related Technologies, Ministry of Education. Shanghai 200237, PR China.
Corresponding author e-mail: yangzhen@ecust.edu.cn.
Information Science Institute, East China University of Science and Technology. Shanghai 200237,PR China.

Computers, Materials & Continua 2009, 13(2), 135-152. https://doi.org/10.3970/cmc.2009.013.135

Abstract

Supercapacitors, also called electrical double-layer capacitors (EDLCs), occupy a region between batteries and dielectric capacitors on the Ragone plot describing the relation between energy and power. BET specific surface area and specific capacitance are two important electrochemical property parameters for activated carbon EDLCs, which are usually tested by experimental method. However, it is misspent time to repeat lots of experiments for EDLCs' studies. In this investigation, we developed one theoretical model based on improved particle swarm optimization algorithm back propagation (PSO-BP) neural network (NN) to simulate and optimize BET specific surface area and specific capacitance. Comparative studies between the predicted data and experimental data-earlier deduced by Liu et al, have revealed that improved PSO-BPNN model bears higher prediction accuracy, faster computation speed and better generalization performance.It is concluded that the improved PSO-BP NN is one simple and effective method to find optimal conditions of BET specific surface area and specific capacitance for activated carbon EDLCs.

Keywords

Activated Carbon EDLC, Electrochemical Property, Neural Network, Particle Swarm Optimization

Cite This Article

X. . Liang, Z. . Yang, X. . Gu and L. . Ling, "Research on activated carbon supercapacitors electrochemical properties based on improved pso-bp neural network," Computers, Materials & Continua, vol. 13, no.2, pp. 135–152, 2009.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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