Limits...
A novel memristive multilayer feedforward small-world neural network with its applications in PID control.

Dong Z, Duan S, Hu X, Wang L, Li H - ScientificWorldJournal (2014)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: School of Electronics and Information Engineering, Southwest University, Chongqing 400715, China.

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.

Show MeSH
Schematic model of the HP memristor.
© Copyright Policy - open-access
Related In: Results  -  Collection


getmorefigures.php?uid=PMC4150482&req=5

fig1: Schematic model of the HP memristor.

Mentions: A memristor or memristive device is essentially a two-terminal passive electronic element with memory capacity. Its memristance state depends on the amplitude, polarity, and duration of the external applied power. The physical model of the HP memristor from [28], shown in Figure 1, consists of a two-layer thin film (thickness D ≈ 10 nm) of TiO2 sandwiched between two platinum electrodes. One of the layers, which is described as TiO2−x, is doped with oxygen vacancies (called dopants) and thus it exhibits high conductivity. The width w of the doped region is modulated depending on the amount of electric charge passing through the memristor. The other TiO2 layer owning an insulating property has a perfect 2 : 1 oxygen-to-titanium ratio, and this layer is referred to the undoped region. Generally, an external excitation v(t) applied across the memristor may cause the charged dopants to drift and the boundary between the two regions would be moved correspondingly with the total memristance changed eventually.


A novel memristive multilayer feedforward small-world neural network with its applications in PID control.

Dong Z, Duan S, Hu X, Wang L, Li H - ScientificWorldJournal (2014)

Schematic model of the HP memristor.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4150482&req=5

fig1: Schematic model of the HP memristor.
Mentions: A memristor or memristive device is essentially a two-terminal passive electronic element with memory capacity. Its memristance state depends on the amplitude, polarity, and duration of the external applied power. The physical model of the HP memristor from [28], shown in Figure 1, consists of a two-layer thin film (thickness D ≈ 10 nm) of TiO2 sandwiched between two platinum electrodes. One of the layers, which is described as TiO2−x, is doped with oxygen vacancies (called dopants) and thus it exhibits high conductivity. The width w of the doped region is modulated depending on the amount of electric charge passing through the memristor. The other TiO2 layer owning an insulating property has a perfect 2 : 1 oxygen-to-titanium ratio, and this layer is referred to the undoped region. Generally, an external excitation v(t) applied across the memristor may cause the charged dopants to drift and the boundary between the two regions would be moved correspondingly with the total memristance changed eventually.

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: School of Electronics and Information Engineering, Southwest University, Chongqing 400715, China.

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.

Show MeSH