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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 principle diagram of the memristive activation function of the output layer. (a) The Simulink model of the memristive activation function of the output layer. (b) The curve of the memristive activation function y(x).
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fig10: The principle diagram of the memristive activation function of the output layer. (a) The Simulink model of the memristive activation function of the output layer. (b) The curve of the memristive activation function y(x).

Mentions: Similarly, Figure 10(a) is the constructing principle diagram of the activation function of the output layer. In the parameter adjustment part (the red dotted line frame), K3 is an adjustable gain and K4 is the fixed gain whose value is K4 = 2 × 10−4. The parameters are the same with the simulation in Figure 9. Figure 10(b) shows the memristive activation function of the output layer. Obviously, as the value of K3 increases, the graphs tend to flatten.


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 principle diagram of the memristive activation function of the output layer. (a) The Simulink model of the memristive activation function of the output layer. (b) The curve of the memristive activation function y(x).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig10: The principle diagram of the memristive activation function of the output layer. (a) The Simulink model of the memristive activation function of the output layer. (b) The curve of the memristive activation function y(x).
Mentions: Similarly, Figure 10(a) is the constructing principle diagram of the activation function of the output layer. In the parameter adjustment part (the red dotted line frame), K3 is an adjustable gain and K4 is the fixed gain whose value is K4 = 2 × 10−4. The parameters are the same with the simulation in Figure 9. Figure 10(b) shows the memristive activation function of the output layer. Obviously, as the value of K3 increases, the graphs tend to flatten.

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