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

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 constrain...

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Bibliographic Details
Published inAxioms Vol. 12; no. 9; p. 815
Main Authors Fan, Shuxian, Li, Meixuan
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.09.2023
Subjects
actuator saturation
Actuators
Algorithms
Calculus
Closed loop systems
Communication
Convexity
FONNs
fractional-order neural networks
Laplace transforms
Mathematical research
Neural networks
Optimization
quantized control
refined sector condition
RSC
Synchronism
China
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Summary: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.
ISSN:2075-1680
2075-1680
DOI:10.3390/axioms12090815