Table of Contents Author Guidelines Submit a Manuscript
The Scientific World Journal
Volume 2014 (2014), Article ID 394828, 12 pages
Research Article

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

1School of Electronics and Information Engineering, Southwest University, Chongqing 400715, China
2Department of MBE, City University of Hong Kong, Hong Kong
3Department of ECE, University of Pittsburgh, Pittsburgh, PA 15261, USA

Received 16 June 2014; Accepted 17 July 2014; Published 14 August 2014

Academic Editor: Jinde Cao

Copyright © 2014 Zhekang Dong et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.