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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 influence of different values of the integer P on the memristor. (a) Joglekar window function for P = 1, P = 3, P = 5, and P = 10. (b) Relationship between memristance versus charge for the nonlinear memristor model. As the integer P increases, the graphs tend to linearity.
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fig2: The influence of different values of the integer P on the memristor. (a) Joglekar window function for P = 1, P = 3, P = 5, and P = 10. (b) Relationship between memristance versus charge for the nonlinear memristor model. As the integer P increases, the graphs tend to linearity.

Mentions: Figure 2(a) exhibits the behavior of the Joglekar window function for different values of P. Figure 2(b) shows the graphs of the memristance versus charge of the memristor. As the value of P becomes smaller, the nonlinearity increases. On the other hand, as the integer P increases, the model tends to the linear model. Based upon this, as well as the literature [3, 28], we set the value of the integer P = 1 in this window function and obtain(6)f(x)=4x−4x2.


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 influence of different values of the integer P on the memristor. (a) Joglekar window function for P = 1, P = 3, P = 5, and P = 10. (b) Relationship between memristance versus charge for the nonlinear memristor model. As the integer P increases, the graphs tend to linearity.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig2: The influence of different values of the integer P on the memristor. (a) Joglekar window function for P = 1, P = 3, P = 5, and P = 10. (b) Relationship between memristance versus charge for the nonlinear memristor model. As the integer P increases, the graphs tend to linearity.
Mentions: Figure 2(a) exhibits the behavior of the Joglekar window function for different values of P. Figure 2(b) shows the graphs of the memristance versus charge of the memristor. As the value of P becomes smaller, the nonlinearity increases. On the other hand, as the integer P increases, the model tends to the linear model. Based upon this, as well as the literature [3, 28], we set the value of the integer P = 1 in this window function and obtain(6)f(x)=4x−4x2.

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