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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 results of the memristor Simulink model. (a) The input current source. (b) Relationship between the current i and the voltage v. (c) Relationship between the memristance M and the charge q. (d) Relationship between the memristance M and the voltage v.
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fig5: The results of the memristor Simulink model. (a) The input current source. (b) Relationship between the current i and the voltage v. (c) Relationship between the memristance M and the charge q. (d) Relationship between the memristance M and the voltage v.

Mentions: The simulation results are exhibited in Figure 5. The current flowing through the memristor is shown in Figure 5(a). The typical hysteresis loop in Figure 5(b) shows its switching characteristic; that is, the memristance can switch between high resistance and low resistance. Figure 5(c) illustrates that the memristance is a nonlinear function of the flow of charge as discussed previously. Figure 5(d) shows the relationship between the memristance M and the charge q. Notably, in the part of the higher memristance state, the change ratio of the memristance is low, while, in the part of the lower memristance state, the change ratio of the memristance is high.


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 results of the memristor Simulink model. (a) The input current source. (b) Relationship between the current i and the voltage v. (c) Relationship between the memristance M and the charge q. (d) Relationship between the memristance M and the voltage v.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig5: The results of the memristor Simulink model. (a) The input current source. (b) Relationship between the current i and the voltage v. (c) Relationship between the memristance M and the charge q. (d) Relationship between the memristance M and the voltage v.
Mentions: The simulation results are exhibited in Figure 5. The current flowing through the memristor is shown in Figure 5(a). The typical hysteresis loop in Figure 5(b) shows its switching characteristic; that is, the memristance can switch between high resistance and low resistance. Figure 5(c) illustrates that the memristance is a nonlinear function of the flow of charge as discussed previously. Figure 5(d) shows the relationship between the memristance M and the charge q. Notably, in the part of the higher memristance state, the change ratio of the memristance is low, while, in the part of the lower memristance state, the change ratio of the memristance is high.

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