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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 nonlinear memristor model and its characteristic curves. (a) The equivalent circuit of the memristor and its 3D symbol. (b) The relationship between memristance and the charge.
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fig3: The nonlinear memristor model and its characteristic curves. (a) The equivalent circuit of the memristor and its 3D symbol. (b) The relationship between memristance and the charge.

Mentions: Giving a sine stimulus to the memristor, we get the simulation results using MATLAB software. It is noteworthy that the memristor is a two-terminal element with polarity, which is shown in Figure 3(a). When the current flows into the memristive device from the positive pole to the negative pole, one can get the relationship curve (the blue line) between memristance and charge through it as shown in Figure 3(b). On the contrary, when the current flows into the memristor from the negative pole to the positive pole, the relationship curve is denoted by the red dashed line. When the charge is close to or exceeds the charge threshold values, the resistance of the memristor reaches and stays at Ron⁡ and Roff⁡, respectively. Notably, the threshold value denotes the quantity of electric charge required when the memristance reaches the limit resistance. The parameters of the model are Ron⁡ = 100 Ω, Roff⁡ = 20 kΩ, M0 = 10 kΩ, D = 10 nm, and μν ≈ 10−14 m2s−1V−1. Moreover, the simulation results in Figure 3(b) are consistent with the results concluded by Adhikari et al. in [8, 29].


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 nonlinear memristor model and its characteristic curves. (a) The equivalent circuit of the memristor and its 3D symbol. (b) The relationship between memristance and the charge.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig3: The nonlinear memristor model and its characteristic curves. (a) The equivalent circuit of the memristor and its 3D symbol. (b) The relationship between memristance and the charge.
Mentions: Giving a sine stimulus to the memristor, we get the simulation results using MATLAB software. It is noteworthy that the memristor is a two-terminal element with polarity, which is shown in Figure 3(a). When the current flows into the memristive device from the positive pole to the negative pole, one can get the relationship curve (the blue line) between memristance and charge through it as shown in Figure 3(b). On the contrary, when the current flows into the memristor from the negative pole to the positive pole, the relationship curve is denoted by the red dashed line. When the charge is close to or exceeds the charge threshold values, the resistance of the memristor reaches and stays at Ron⁡ and Roff⁡, respectively. Notably, the threshold value denotes the quantity of electric charge required when the memristance reaches the limit resistance. The parameters of the model are Ron⁡ = 100 Ω, Roff⁡ = 20 kΩ, M0 = 10 kΩ, D = 10 nm, and μν ≈ 10−14 m2s−1V−1. Moreover, the simulation results in Figure 3(b) are consistent with the results concluded by Adhikari et al. in [8, 29].

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