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.
authors.affiliation: School of Electronics and Information Engineering, Southwest University, Chongqing 400715, China.
authors.forename: Zhekang
authors.initials: Z
authors.lastname: Dong
authors.affiliation: School of Electronics and Information Engineering, Southwest University, Chongqing 400715, China.
authors.forename: Shukai
authors.initials: S
authors.lastname: Duan
authors.affiliation: Department of MBE, City University of Hong Kong, Hong Kong.
authors.forename: Xiaofang
authors.initials: X
authors.lastname: Hu
authors.affiliation: School of Electronics and Information Engineering, Southwest University, Chongqing 400715, China.
authors.forename: Lidan
authors.initials: L
authors.lastname: Wang
authors.affiliation: Department of ECE, University of Pittsburgh, Pittsburgh, PA 15261, USA.
authors.forename: Hai
authors.identifier: 0000-0002-5866-1966
authors.initials: H
authors.lastname: Li
mesh_terms: D000465:Algorithms; D016571:Neural Networks, Computer