Preset-Time Convergence Fuzzy Zeroing Neural Network for Chaotic System Synchronization: FPGA Validation and Secure...

Area of Science:

• Nonlinear Dynamics and Control Systems
• Computational Intelligence and Neural Networks
• Secure Communication Systems

Background:

• Chaotic systems exhibit high sensitivity to initial conditions and complex dynamics, limiting their practical applications.
• Synchronization of chaotic systems is vital for secure data transmission, especially in sensor networks.
• Aperiodic parameter excitation poses significant challenges for controlling chaotic systems.

Purpose of the Study:

• To propose a novel preset-time fuzzy zeroing neural network (PTCFZNN) model for achieving chaotic synchronization.
• To address the complexities of aperiodic parameter excitation in chaotic systems.
• To validate the practical feasibility and application value of the proposed model.

Main Methods:

• Development of a Takagi-Sugeno fuzzy control-based PTCFZNN model.
• Design of the neural network to handle complex dynamic variations in chaotic systems.
• Implementation and verification using Field-Programmable Gate Array (FPGA) experiments.

Main Results:

• Successful achievement of chaotic synchronization in aperiodic parameter exciting chaotic systems.
• Hardware verification via FPGA experiments confirmed the model's practical engineering feasibility.
• Demonstrated effectiveness in chaos-masking communication, enhancing confidentiality and anti-jamming capabilities.

Conclusions:

• The PTCFZNN model effectively achieves chaotic synchronization for aperiodic parameter exciting systems.
• FPGA implementation validates the model's suitability for real-world engineering applications.
• The model offers significant value for securing sensor data transmission through chaos-masking communication.