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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.

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The relationship curve between the rate of the memristive conductance change and the current.
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fig7: The relationship curve between the rate of the memristive conductance change and the current.

Mentions: Differentiating (15) with respect to time t, we can be obtain(16)dG(t)dt=4kAe4kq(t)ΔR(Ron⁡Ae4kq(t)+Roff⁡)2×dq(t)dt,where the current i(t) = dq(t)/dt. Notably, when Δt → 0, dG(t) ≈ ΔG. Hence, the rate of the memristive conductance ΔG can be described as the synapse weight update rule. The relationship curve between the rate of the memristive conductance change and the current is shown in Figure 7. When the current is tiny, the memristive conductance is almost invariant. While the current tends to ±4 mA, the memristive conductance changes suddenly. So the current threshold value of the memristive synapse can be set as /Ith/ = 4 mA.


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)

The relationship curve between the rate of the memristive conductance change and the current.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig7: The relationship curve between the rate of the memristive conductance change and the current.
Mentions: Differentiating (15) with respect to time t, we can be obtain(16)dG(t)dt=4kAe4kq(t)ΔR(Ron⁡Ae4kq(t)+Roff⁡)2×dq(t)dt,where the current i(t) = dq(t)/dt. Notably, when Δt → 0, dG(t) ≈ ΔG. Hence, the rate of the memristive conductance ΔG can be described as the synapse weight update rule. The relationship curve between the rate of the memristive conductance change and the current is shown in Figure 7. When the current is tiny, the memristive conductance is almost invariant. While the current tends to ±4 mA, the memristive conductance changes suddenly. So the current threshold value of the memristive synapse can be set as /Ith/ = 4 mA.

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