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Projection synchronization method of fractional order complex value memristor neural network and application thereof

A neural network and complex-valued technology, applied in the field of information and communication science, can solve the problems of difficult projection synchronization of fractional complex-valued memristive neural networks, and achieve the effects of accurate results, strong practicability, and systematic analysis.

Active Publication Date: 2021-08-06
ANHUI UNIVERSITY
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  • Abstract
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  • Claims
  • Application Information

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Problems solved by technology

[0007] In order to solve the problem that the improved fractional-order complex-valued memristive neural network with time-varying delay is difficult to realize projection synchronization within a limited time in the prior art, the present invention provides a projection synchronization of fractional-order complex-valued memristive neural network and its application

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  • Projection synchronization method of fractional order complex value memristor neural network and application thereof
  • Projection synchronization method of fractional order complex value memristor neural network and application thereof
  • Projection synchronization method of fractional order complex value memristor neural network and application thereof

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Embodiment 1

[0116] Such as figure 1 As shown, this embodiment provides a projection synchronization method of a fractional-order complex-valued memristive neural network, which is used to realize the projection synchronization of the driving network and the response network of the fractional-order complex-valued memristive neural network system within a limited time, The projection synchronization method includes the following steps:

[0117] Step S1: Design the synchronous controller u i (t), synchronous controller u i The design method of (t) comprises steps:

[0118] Step S11: Define the synchronization error e of the system i (t), representing the synchronization error e i The function of (t) is:

[0119] e i (t)=y i (t)-νx i (t)

[0120] where x i (t) represents the state variable driving the network; y i (t) represents the state variable of the response network; ν represents the projection factor, which reflects the synchronous proportional relationship between the drive ...

Embodiment 2

[0165] On the basis of Embodiment 1, this embodiment provides a construction method of a fractional-order complex-valued memristive neural network system, wherein the fractional-order complex-valued memristive neural network system includes a driving network and a response network, and the construction method is constructed In the fractional-order complex-valued memristive neural network system, in order to achieve projection synchronization between the drive network and the response network within a limited time, the following steps are included:

[0166] Step S1: Design the synchronous controller u i (t), synchronous controller u i The design method of (t) comprises steps:

[0167] Step S11: Define the synchronization error e of the system i (t), representing the synchronization error e i The function of (t) is:

[0168] e i (t)=y i (t)-νx i (t)

[0169] where x i (t) represents the state variable driving the network; y i (t) represents the state variable of the re...

Embodiment 3

[0205] This embodiment mainly includes two parts:

[0206] One is to theoretically prove the effectiveness of the synchronization controller designed in the projection synchronization method for providing fractional-order complex-valued memristive neural network in Embodiment 1.

[0207] The second is to verify the performance of the drive network and the response network to achieve projection synchronization within a limited time for the fractional-order complex-valued memristive neural network system constructed in Example 2 by means of numerical simulation.

[0208] (theoretical proof and simulation experiments are not used to limit the present invention, in other embodiments, simulation experiments may not be carried out, and other experimental schemes may also be used for testing to verify the performance of the neural network system.)

[0209] 1. Theoretical Proof

[0210] 1. Conditional assumptions: First, without loss of generality, there are two ways to solve complex...

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Abstract

The invention belongs to the field of information and communication science, and particularly relates to a projection synchronization method of a fractional order complex value memristor neural network and application thereof. The method is used for realizing projection synchronization of a fractional order complex value memristor neural network system, and comprises the following steps: S1, designing a synchronization controller, and the design method of the synchronization controller comprises the following steps: S11, defining a synchronization error of the system; S12, the synchronization error is divided into a real part error and an imaginary part error; S13, according to the separated real part error and imaginary part error; designing a synchronous controller through two parts, namely a real part self-adaptive controller and an imaginary part self-adaptive controller; and S2, introducing a real part adaptive controller into a model representing the real part of the response network; and introducing an imaginary part adaptive controller into a model characterizing an imaginary part of the response network. The problem that an improved fractional order complex value memristor neural network with time-varying delay is difficult to realize projection synchronization in the prior art is solved.

Description

technical field [0001] The invention belongs to the field of information and communication sciences, and specifically relates to a projection synchronization method of a fractional-order complex-valued memristive neural network, a construction method of a fractional-order complex-valued memristive neural network system, and a fractional-order complex-valued memristive neural network system. A projection synchronization device for a network, a projection synchronization terminal for a fractional complex-valued memristive neural network, and an application of a projection synchronization method for a fractional complex-valued memristive neural network. Background technique [0002] The memristor is the fourth electrical component. The electrical characteristics of the memristor include: the tight hysteresis loop shrinks to the origin; the excitation frequency is negatively correlated with the hysteresis side lobe area; when the excitation frequency tends to infinity, the tight ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G05B13/04
CPCG05B13/042
Inventor 张红伟徐胜涛赵安祥赵余懿李士保丁大为
Owner ANHUI UNIVERSITY
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