A Novel PID Neural Network Controller based on Memristor

被引：0

作者：

Geng Yangyang ^{[1
]}

Duan Shukai ^{[1
]}

Dong Zhekang ^{[2
]}

Wang Lidan ^{[1
]}

机构：

[1] Southwest Univ, Coll Elect & Informat Engn, Chongqing 400715, Peoples R China

[2] Zhejiang Univ, Coll Elect Engn, Hangzhou 310027, Zhejiang, Peoples R China

来源：

PROCEEDINGS OF THE 36TH CHINESE CONTROL CONFERENCE (CCC 2017) | 2017年

基金：

中国国家自然科学基金;

关键词：

Memristor; adaptive PID neural network; Chaotic Particle Swarm Optimization algorithm; automatic voltage regulation system;

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：

In this paper, an effective optimize method based on adaptive PID neural network controller is presented. Chaotic Particle Swarm Optimization (CPSO) is introduced to initialize the parameters of neural network for improving the convergent speed and preventing weights trapping into local optima. In order to realize the hardware platform of PID neural network controller and facilitate weight update algorithm, the nanoscale memristor is utilized to analog electronic synapse. Meanwhile, the stability of closed-loop system is further proved by Lyapunov theory. Based on several comparative experiments on automatic voltage regulation system, the final results illustrate that the proposed controller can obtain better precision in a shorter time.

引用

页码：3988 / 3993

页数：6

共 50 条

[21] A memristor-based convolutional neural network with full parallelization architecture [J].

Sun, Sheng-Yang ;

Li, Zhiwei ;

Li, Jiwei ;

Liu, Husheng ;

Liu, Haijun ;

Li, Qingjiang .

IEICE ELECTRONICS EXPRESS, 2019, 16 (03)

[22] Memristor-based Bayesian spiking neural network for IBD diagnosis [J].

Li, Xiaowen ;

Wu, Qiqiao ;

Chen, Yuanwen ;

Jin, Yang ;

Ma, Jianxia ;

Yang, Jianguo .

KNOWLEDGE-BASED SYSTEMS, 2024, 300

[23] Predicting House Price With a Memristor-Based Artificial Neural Network [J].

Wang, J. J. ;

Hu, S. G. ;

Zhan, X. T. ;

Luo, Q. ;

Yu, Q. ;

Liu, Zhen ;

Chen, T. P. ;

Yin, Y. ;

Hosaka, Sumio ;

Liu, Y. .

IEEE ACCESS, 2018, 6 :16523-16528

[24] Memristor-Based Neural Network Circuit of Delay and Simultaneous Conditioning [J].

Xu, Xinyu ;

Xu, Weilin ;

Wei, Baolin ;

Hu, Fangrong .

IEEE ACCESS, 2021, 9 :148933-148947

[25] Memristor Bridge Synapse-Based Neural Network and Its Learning [J].

Adhikari, Shyam Prasad ;

Yang, Changju ;

Kim, Hyongsuk ;

Chua, Leon O. .

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2012, 23 (09) :1426-1435

[26] An active memristor based rate-coded spiking neural network [J].

Fida, Aabid Amin ;

Khanday, Farooq A. ;

Mittal, Sparsh .

NEUROCOMPUTING, 2023, 533 :61-71

[27] Compressive Strength Prediction of High-Performance Hydraulic Concrete using a Novel Neural Network Based on the Memristor [J].

Lu, Jun ;

Qiu, Lin ;

Liang, Yingjie ;

Lin, Ji .

ADVANCES IN APPLIED MATHEMATICS AND MECHANICS, 2024,

[28] A novel memristor Hopfield neural network with homogeneous coexisting multi-scroll attractors [J].

Huang, Lilian ;

Chen, Fangyi ;

Yang, Yan ;

Zheng, Lei .

PHYSICA SCRIPTA, 2024, 99 (06)

[29] On Protecting Neural Network Model in Memristor Platforms [J].

Jin S. ;

Wang Y.-H. .

Jisuanji Xuebao/Chinese Journal of Computers, 2022, 45 (11) :2377-2392

[30] On the validity of memristor modeling in the neural network literature [J].

Pershin, Yuriy V. ;

Di Ventra, Massimiliano .

NEURAL NETWORKS, 2020, 121 (121) :52-56

← 1 2 3 4 5 →