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

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

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...

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Main Authors: Zhekang Dong, Shukai Duan, Xiaofang Hu, Lidan Wang, Hai Li
Format: Article
Language:English
Published: Hindawi Limited 2014-01-01
Series:The Scientific World Journal
Online Access:http://dx.doi.org/10.1155/2014/394828
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spelling doaj-210ad84467ca4e68b8e345603bfaaf3a2020-11-24T21:26:25ZengHindawi LimitedThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/394828394828A Novel Memristive Multilayer Feedforward Small-World Neural Network with Its Applications in PID ControlZhekang Dong0Shukai Duan1Xiaofang Hu2Lidan Wang3Hai Li4School of Electronics and Information Engineering, Southwest University, Chongqing 400715, ChinaSchool of Electronics and Information Engineering, Southwest University, Chongqing 400715, ChinaDepartment of MBE, City University of Hong Kong, Hong KongSchool of Electronics and Information Engineering, Southwest University, Chongqing 400715, ChinaDepartment of ECE, University of Pittsburgh, Pittsburgh, PA 15261, USAIn 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.http://dx.doi.org/10.1155/2014/394828
collection DOAJ
language English
format Article
sources DOAJ
author Zhekang Dong
Shukai Duan
Xiaofang Hu
Lidan Wang
Hai Li
spellingShingle Zhekang Dong
Shukai Duan
Xiaofang Hu
Lidan Wang
Hai Li
A Novel Memristive Multilayer Feedforward Small-World Neural Network with Its Applications in PID Control
The Scientific World Journal
author_facet Zhekang Dong
Shukai Duan
Xiaofang Hu
Lidan Wang
Hai Li
author_sort Zhekang Dong
title A Novel Memristive Multilayer Feedforward Small-World Neural Network with Its Applications in PID Control
title_short A Novel Memristive Multilayer Feedforward Small-World Neural Network with Its Applications in PID Control
title_full A Novel Memristive Multilayer Feedforward Small-World Neural Network with Its Applications in PID Control
title_fullStr A Novel Memristive Multilayer Feedforward Small-World Neural Network with Its Applications in PID Control
title_full_unstemmed A Novel Memristive Multilayer Feedforward Small-World Neural Network with Its Applications in PID Control
title_sort novel memristive multilayer feedforward small-world neural network with its applications in pid control
publisher Hindawi Limited
series The Scientific World Journal
issn 2356-6140
1537-744X
publishDate 2014-01-01
description 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.
url http://dx.doi.org/10.1155/2014/394828
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