Exponential Synchronization of Switched Neural Networks With Mixed Time-Varying Delays via Static/Dynamic Event-Triggering Rules

Cao, Yuting; Wang, Shiqin; Wen, Shiping

doi:10.1109/access.2019.2955939

Cited by 15 publications

(2 citation statements)

References 67 publications

(55 reference statements)

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“…Event-triggered control only transmits information when the measured error exceeds the threshold, and it can decrease the frequency of information exchange and improve the efficiency of resource utilization. Very recently, the dynamic behaviors of various neural networks have been widely studied via event-triggered control [32][33][34][35][36][37][38]. For instance, by designing a dynamic eventtriggered output feedback controller, Fei et al [32] investigated the exponential stability for linear switched systems with frequent asynchronism.…”

Section: Introductionmentioning

confidence: 99%

Exponential Synchronization of Memristive Neural Networks with Discrete and Distributed Time-Varying Delays via Event-Triggered Control

Wen-bo

2021

Discrete Dynamics in Nature and Society

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In this paper, we investigate the exponential synchronization problem of memristive neural networks (MNNs) with discrete and distributed time-varying delays under event-triggered control. An event-triggered controller with the static and dynamic event-triggering conditions is designed to improve the efficiency of resource utilization. By constructing a new Lyapunov function, some sufficient criteria are obtained to realize the exponential synchronization of considered drive-response MNNs under the designed event-triggered controller. In addition, the Zeno behavior will not occur by proving that the event-triggering interval has a positive lower bound under different event-triggering conditions. Finally, a numerical example is provided to prove the validity of our theoretical results.

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Section: Introductionmentioning

confidence: 99%

Exponential Synchronization of Memristive Neural Networks with Discrete and Distributed Time-Varying Delays via Event-Triggered Control

Wen-bo

2021

Discrete Dynamics in Nature and Society

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“…DL consists of multiple layers of neural networks; these networks are mathematical models inspired by the connectivity patterns of biological neural networks. This neural network-based model is distributed and is intellectually capable of processing information [12], [13]. DL also can automatically learn optimal features for given tasks from the input dataset.…”

Section: Introductionmentioning

confidence: 99%

Direct Connection-Based Convolutional Neural Network (DC-CNN) for Fault Diagnosis of Rotor Systems

Kim

Jung

et al. 2020

IEEE Access

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Fault diagnosis of rotor systems is important to prevent unexpected failures. Recently, deep learning (DL) methods, such as a convolutional neural network (CNN), have been utilized in many research areas, including fault diagnosis. DL has gained significant attention thanks to its ability to efficiently learn proper features from input data. It is possible to learn enriched hierarchical features by making the DL architectures deeper; therefore, many studies have been conducted to stack the neural networks, which are the basic building blocks of DL, deeper. However, it becomes difficult to comprehensively train neural network architectures as they become deeper, due to problems in the flow of gradient information during the training phase. In this paper, a direct connection based CNN (DC-CNN) method is proposed to significantly improve training efficiency and diagnosis performance. DC-CNN connects feature maps of different layers within a CNN to improve the gradient information flow over the layers. These additional connections, however, can increase the number of trainable parameters within the network. To prevent problems that might be caused by an increased number of parameters, dimension reduction modules are also developed. Moreover, to consider the anisotropic characteristics inherent in rotor systems, the vibration images containing both spatial and temporal information are generated and utilized. The effectiveness of DC-CNN is validated using experimental data from a rotor testbed. The experimental results indicate that the proposed method outperforms other conventional approaches with a smaller number of parameters. Also, visualizations of the learned features indicate that the proposed method can learn much more effective and significant features. Furthermore, the proposed method outperforms other approaches under conditions of insufficient or noisy data.

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Design of a Two-Channel Quadrature Mirror Filter Bank Through a Diversity-Driven Multi-Parent Evolutionary Algorithm

Chauhan

Singh

Aggarwal

2021

Circuits Syst Signal Process

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Exponential Synchronization of Switched Neural Networks With Mixed Time-Varying Delays via Static/Dynamic Event-Triggering Rules

Cited by 15 publications

References 67 publications

Exponential Synchronization of Memristive Neural Networks with Discrete and Distributed Time-Varying Delays via Event-Triggered Control

Exponential Synchronization of Memristive Neural Networks with Discrete and Distributed Time-Varying Delays via Event-Triggered Control

Direct Connection-Based Convolutional Neural Network (DC-CNN) for Fault Diagnosis of Rotor Systems

Design of a Two-Channel Quadrature Mirror Filter Bank Through a Diversity-Driven Multi-Parent Evolutionary Algorithm

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