Sliding mode control for finite-time and fixed-time synchronization of delayed complex-valued recurrent neural networks with discontinuous activation functions and nonidentical parameters

Aouiti, Chaouki; Bessifi, Mayssa

doi:10.1016/j.ejcon.2021.01.006

Cited by 26 publications

(2 citation statements)

References 53 publications

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“…Song et al [10] discussed the synchronization problem of fractional CVNN with reaction-diffusion term in finite time interval. Aouiti et al [11] considered the finite time and fixed time synchronization problem of time-delay recurrent CVNN controlled by sliding mode with discontinuous activation function and different parameters.…”

Section: Introductionmentioning

confidence: 99%

Coordinate Transformation of Buoy Based on Fractional Complex Valued Neural Network

Guo¹,

Guo²

2022

Journal of Compurters

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<p>To reduce the error of coordinate transformation, a fractional order convex valued neural network (FCVNN) is explored. The convergence is proved. We take the longitude as the real part of the input, and take the latitude as the imaginary part of the input. Thus, we construct the complex valued input of the CVNN. All longitudes and latitudes on the earth are perpendicular to each other. This satisfies that the real part and imaginary part of the complex form an orthogonal unit basis. Input Xiamen 92 space coordinates and WGS-84 space coordinates obtained from the transformation as the training samples. The weights of neural network are updated. Input the geodetic coordinates of test data and output the geodetic coordinates of WGS84 corresponding to the results. FCVNN is applied to Xiamen 92 coordinate transformation, and the transformation accuracy is improved. Using the orthogonality of longitude and latitude, CVNN are constructively used to solve the coordinate transformation problem. Many insights can be transferred from real domain to complex domain. If the data exists naturally in the complex domain, or can be meaningfully moved to the complex plane, the complex neural network should be used for the task. </p> <p> </p>

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Section: Introductionmentioning

confidence: 99%

Coordinate Transformation of Buoy Based on Fractional Complex Valued Neural Network

Guo¹,

Guo²

2022

Journal of Compurters

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“…It is worth mentioning that CDNs can be expressed by diferential equations demonstrating various dynamical behaviors such as self-organization, spatial-temporal chaos, synchronization, and spiral waves. Among these, synchronization is a key issue that has been extensively investigated [4][5][6]; it fnds applications through synchronous communication and signals synchronization in geostationary satellites, synchronous motors, and databases. It is well known that the process of synchronization between two or more nodes aims to attain common trajectories by tuning a set of prescribed properties.…”

Section: Introductionmentioning

confidence: 99%

Nonuniform Sampled‐Data Control for Synchronization of Semi‐Markovian Jump Stochastic Complex Dynamical Networks with Time‐Varying Delays

Sakthivel

Devi

et al. 2022

Complexity

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In this paper, the problem of exponential synchronization of semi-Markov jump stochastic complex dynamical networks using nonuniform sampled-data control with random delayed information exchanges among dynamical nodes are discussed. In particular, it is considered that random delayed information exchanges follow a Bernoulli distribution, in which stochastic variables are used to model randomness. To achieve exponential synchronization, we designed a nonuniform sampled-data control approach. By constructing an appropriate Lyapunov–Krasovskii functional and using the Wirtinger inequality, sufficient criteria were obtained in terms of linear matrix inequalities. Finally, numerical examples were implemented to demonstrate the effectiveness and superiority of the proposed design techniques.

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Introduction

Zhang¹,

Wang²,

Chen³

et al. 2022

Complex-Valued Neural Networks Systems With Time Delay

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Sliding mode control for finite-time and fixed-time synchronization of delayed complex-valued recurrent neural networks with discontinuous activation functions and nonidentical parameters

Cited by 26 publications

References 53 publications

Coordinate Transformation of Buoy Based on Fractional Complex Valued Neural Network

Coordinate Transformation of Buoy Based on Fractional Complex Valued Neural Network

Nonuniform Sampled‐Data Control for Synchronization of Semi‐Markovian Jump Stochastic Complex Dynamical Networks with Time‐Varying Delays

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