ArticleSensors (Basel, Switzerland)2025
Preset-Time Convergence Fuzzy Zeroing Neural Network for Chaotic System Synchronization: FPGA Validation and Secure Communication Applications.
Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- From Schreier graphs to secure s-boxes using a group-theoretic design framework.Scientific reports · 2026Article
- A deep learning approach for solving a fractional order Monkeypox transmission model using a harmonic neural network optimized with SGDM.Scientific reports · 2026Article
- PAC-ZNN for Robust Target Tracking in WSNs Against Complex Polynomial Noise.Sensors (Basel, Switzerland) · 2026Article
- Fixed-point topology meets fractal memory: a Kutumba-stabilized framework for nonlocal fractal-fractional dynamics.Scientific reports · 2026Article
- FPGA Implementation of a Radar-Based Fall Detection System Using Binarized Convolutional Neural Networks.Sensors (Basel, Switzerland) · 2026Article
- Anonymous and Efficient Chaotic Map-Based Authentication Protocol for Industrial Internet of Things.Sensors (Basel, Switzerland) · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Chaotic systems, characterized by extreme sensitivity to initial conditions and complex dynamical behaviors, exhibit significant potential for applications in various fields. Effective control of chaotic system synchronization is particularly crucial in sensor-related applications. This paper proposes a preset-time fuzzy zeroing neural network (PTCFZNN) model based on Takagi-Sugeno fuzzy control to achieve chaotic synchronization in aperiodic parameter exciting chaotic systems. The designed PTCFZNN model accurately handles the complex dynamic variations inherent in chaotic systems, overcoming the challenges posed by aperiodic parameter excitation to achieve synchronization. Additionally, field-programmable gate array (FPGA) verification experiments successfully implemented the PTCFZNN-based chaotic system synchronization control on hardware platforms, confirming its feasibility for practical engineering applications. Furthermore, experimental studies on chaos-masking communication applications of the PTCFZNN-based chaotic system synchronization further validate its effectiveness in enhancing communication confidentiality and anti-jamming capability, highlighting its important application value for securing sensor data transmission.
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Registered trials
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