Event‐triggered synchronisation of Markovian reaction–diffusion inertial neural networks and its application in image encryption

Song, Xiaona; Man, Jingtao; Song, Shuai; Ning, Zhaoke

doi:10.1049/iet-cta.2020.0136

Cited by 13 publications

(5 citation statements)

References 48 publications

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“…Remark The other technique difficulty is how to investigate the stabilisation problem of system (1) with input saturation under quantised control. It should be mentioned that the traditional Lyapunov–Krasovskii stability theory [10, 22–28] and some inequality techniques (e.g. matrix measure) cannot be applied to fractional‐order systems any more.…”

Section: Resultsmentioning

confidence: 99%

“…As information and communications technology have grown, networked control methods, such as sampled‐data control [21, 22], event‐triggered control [23–25], intermittent control [12, 26], and quantised control [10, 27–29], have become a popular research topic in engineering applications. The common feature of these networked control methods is that the control information is digital and discrete.…”

Section: Introductionmentioning

confidence: 99%

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Quantised control for local Mittag–Leffler stabilisation of fractional‐order neural networks with input saturation: A refined sector condition

Fan

Huang

Wang

et al. 2021

IET Control Theory & Appl

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This brief focuses on the local Mittag–Leffler stabilisation of fractional‐order neural networks via quantised control with input saturation. First, a refined sector condition is put forward, which can deal with the problem of the simultaneous existence of quantiser effect and actuator constraint. With the aid of refined sector condition, theoretical analysis on the local Mittag–Leffler stabilisation of the resulting closed‐loop systems is carried out by using some inequality techniques on Mittag–Leffler function and fractional‐order Lyapunov theory. In addition, two convex optimisation schemes are respectively developed to minimise the cost of actuators and to enlarge the admissible initial region. Finally, a three‐neurons fractional‐order neural networks is applied to illustrate the effectiveness of the derived results.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Quantised control for local Mittag–Leffler stabilisation of fractional‐order neural networks with input saturation: A refined sector condition

Fan

Huang

Wang

et al. 2021

IET Control Theory & Appl

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show abstract

“…Remark 2. The local synchronization problem of Systems ( 1) and (2) under actuator saturation and quantized control is challenging to investigate using traditional Lyapunov-Krasovskii stability theory [18,20] and matrix measure techniques due to the presence of fractional-order dynamics. Furthermore, the previous sector condition [30,31] is not available in this case either due to the effects of saturation nonlinearity and quantization.…”

Section: Optimization Algorithmsmentioning

confidence: 99%

“…With the growth of information and communication technology, networked control methods, such as event-triggered control [18], sampled-data control [19,20], intermittent control [17,21], and quantized control [15,[22][23][24], have become increasingly popular in engineering applications. These methods share the common feature of using digital and isolated control intelligence.…”

Section: Introductionmentioning

confidence: 99%

Quantized Control for Local Synchronization of Fractional-Order Neural Networks with Actuator Saturation

Fan,

2023

Axioms

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This brief discusses the use of quantized control with actuator saturation to achieve the local synchronization of master–slave fractional-order neural networks (FONNs). A refined sector condition (RSC) is proposed that addresses the issue of the simultaneous quantizer effects and actuator constraints. The RSC is used in the theoretical analysis of local synchronization in drive-response systems. The analysis employs inequality techniques on the Mittag–Leffler function and fractional-order Lyapunov theory. Additionally, this paper presents two convex optimization algorithms that aim to minimize the actuator’s costs and expand the admissible initial area (AIA). Finally, this paper employs a three-neuron FONN to demonstrate the efficacy of the proposed methods.

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“…In the field of computers, data mining technology is a hot research topic, and time-series analysis and prediction technology are important branches of data mining. Time series refers to a series of observations obtained in the process of production and scientific research and so on, recorded in chronological order, which is a multiple variable at different moments on the formation of random data, reflecting the development of phenomena that change the law [3]. Time series has three main characteristics: randomness, continuity, and periodicity.…”

Section: Introductionmentioning

confidence: 99%

Construction and Simulation of a Strategic HR Decision Model Based on Recurrent Neural Network

Zhang

et al. 2022

Journal of Mathematics

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In this paper, RNN (Recurrent Neural Network) algorithm is used to conduct an in-depth analysis of HR strategic decision-making and an HR strategic decision model is constructed for simulation. The four evaluation dimensions of index screening are extracted, the generalized gray correlation analysis is applied to screen the initial selection indexes of HR strategic decision-making, and then the HR strategic decision-making index system of power supply enterprises is constructed. On this basis, the applicability of the BP neural network in HR strategic decision-making is analysed and demonstrated, a BP neural network-based HR strategic decision-making model for power supply enterprises is constructed, and the rationality of the model is illustrated through the model training and testing. Finally, an empirical study is conducted with S power supply company as an example to illustrate the operability of the BP network model for HR strategic decision-making. The results of this paper provide a scientific basis for human resource decision-making in power supply enterprises and provide theoretical support for promoting the healthy development of power supply enterprises. Improving the level of human resource management can make fuller use of human resources and realize the strategic goals of the enterprise. A strategy for optimizing the training of hybrid convolutional neural networks is proposed, using an exponential linear unit activation function to solve the problem of neuron dead zones, a hybrid pooling strategy is used to improve the problem of feature information loss in maximum pooling processing, and an improved cross-entropy loss function is used to solve the problem of insufficient learning of difficult classification samples. The optimization of the model training process is finally completed, and the recommendation quality is improved. The Bayesian probability distribution table is learned to be filled in the expert data set; finally, the accuracy and effectiveness of the model are tested on the experimental platform. The results of the experiments show that the established Bayesian model beats the platform’s built-in intelligent method with a 78.2% win rate. That is, the model can make intelligent recommendations for strategies in staffed and unmanned platform decision-making and command-and-control combat units to execute tactical actions to achieve the best operational effectiveness.

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Event‐triggered synchronisation of Markovian reaction–diffusion inertial neural networks and its application in image encryption

Cited by 13 publications

References 48 publications

Quantised control for local Mittag–Leffler stabilisation of fractional‐order neural networks with input saturation: A refined sector condition

Quantised control for local Mittag–Leffler stabilisation of fractional‐order neural networks with input saturation: A refined sector condition

Quantized Control for Local Synchronization of Fractional-Order Neural Networks with Actuator Saturation

Construction and Simulation of a Strategic HR Decision Model Based on Recurrent Neural Network

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