A Novel Memristive Multilayer Feedforward Small-World Neural Network with Its Applications in PID Control

Zhekang Dong; Shukai Duan; Xiaofang Hu; Lidan Wang; Hai Li

doi:10.1155/2014/394828

A Novel Memristive Multilayer Feedforward Small-World Neural Network with Its Applications in PID Control

Zhekang Dong
, Shukai Duan^*
, Xiaofang Hu
, Lidan Wang
, Hai Li

^*Corresponding author for this work

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

22 Citations (Scopus)

55 Downloads (CityUHK Scholars)

Abstract

In this paper, we present an implementation scheme of memristor-based multilayer feedforward small-world neural network (MFSNN) inspirited by the lack of the hardware realization of the MFSNN on account of the need of a large number of electronic neurons and synapses. More specially, a mathematical closed-form charge-governed memristor model is presented with derivation procedures and the corresponding Simulink model is presented, which is an essential block for realizing the memristive synapse and the activation function in electronic neurons. Furthermore, we investigate a more intelligent memristive PID controller by incorporating the proposed MFSNN into intelligent PID control based on the advantages of the memristive MFSNN on computation speed and accuracy. Finally, numerical simulations have demonstrated the effectiveness of the proposed scheme.

Original language	English
Article number	394828
Number of pages	12
Journal	TheScientificWorldJournal [electronic resource]
Volume	2014
Online published	14 Aug 2014
DOIs	https://doi.org/10.1155/2014/394828
Publication status	Published - 2014

Funding

The work was supported by Program for New Century Excellent Talents in University (Grant nos. [2013] 47), National Natural Science Foundation of China (Grant nos. 61372139, 61101233, and 60972155), "Spring Sunshine Plan" Research Project of Ministry of Education of China (Grant no. z2011148), Technology Foundation for Selected Overseas Chinese Scholars, Ministry of Personnel in China (Grant no. 2012-186), University Excellent Talents Supporting Foundations in of Chongqing (Grant no. 2011-65), University Key Teacher Supporting Foundations of Chongqing (Grant no. 2011-65), Fundamental Research Funds for the Central Universities (Grant nos. XDJK2014A009, XDJK2013B011).

Research Keywords

ELEMENT
SYNAPSE
MODEL
DRIFT

Publisher's Copyright Statement

This full text is made available under CC-BY 3.0. https://creativecommons.org/licenses/by/3.0/

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title = "A Novel Memristive Multilayer Feedforward Small-World Neural Network with Its Applications in PID Control",

abstract = "In this paper, we present an implementation scheme of memristor-based multilayer feedforward small-world neural network (MFSNN) inspirited by the lack of the hardware realization of the MFSNN on account of the need of a large number of electronic neurons and synapses. More specially, a mathematical closed-form charge-governed memristor model is presented with derivation procedures and the corresponding Simulink model is presented, which is an essential block for realizing the memristive synapse and the activation function in electronic neurons. Furthermore, we investigate a more intelligent memristive PID controller by incorporating the proposed MFSNN into intelligent PID control based on the advantages of the memristive MFSNN on computation speed and accuracy. Finally, numerical simulations have demonstrated the effectiveness of the proposed scheme.",

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AU - Dong, Zhekang

AU - Duan, Shukai

AU - Hu, Xiaofang

AU - Wang, Lidan

AU - Li, Hai

PY - 2014

Y1 - 2014

N2 - In this paper, we present an implementation scheme of memristor-based multilayer feedforward small-world neural network (MFSNN) inspirited by the lack of the hardware realization of the MFSNN on account of the need of a large number of electronic neurons and synapses. More specially, a mathematical closed-form charge-governed memristor model is presented with derivation procedures and the corresponding Simulink model is presented, which is an essential block for realizing the memristive synapse and the activation function in electronic neurons. Furthermore, we investigate a more intelligent memristive PID controller by incorporating the proposed MFSNN into intelligent PID control based on the advantages of the memristive MFSNN on computation speed and accuracy. Finally, numerical simulations have demonstrated the effectiveness of the proposed scheme.

AB - In this paper, we present an implementation scheme of memristor-based multilayer feedforward small-world neural network (MFSNN) inspirited by the lack of the hardware realization of the MFSNN on account of the need of a large number of electronic neurons and synapses. More specially, a mathematical closed-form charge-governed memristor model is presented with derivation procedures and the corresponding Simulink model is presented, which is an essential block for realizing the memristive synapse and the activation function in electronic neurons. Furthermore, we investigate a more intelligent memristive PID controller by incorporating the proposed MFSNN into intelligent PID control based on the advantages of the memristive MFSNN on computation speed and accuracy. Finally, numerical simulations have demonstrated the effectiveness of the proposed scheme.

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KW - SYNAPSE

KW - MODEL

KW - DRIFT

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DO - 10.1155/2014/394828

M3 - RGC 21 - Publication in refereed journal

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JO - TheScientificWorldJournal [electronic resource]

JF - TheScientificWorldJournal [electronic resource]

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A Novel Memristive Multilayer Feedforward Small-World Neural Network with Its Applications in PID Control

Abstract

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Research Keywords

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