Advancements in exponential synchronization and encryption techniques: Quaternion-Valued Artificial Neural Networks...

Area of Science:

• Artificial Intelligence
• Neural Networks
• Complex Systems

Background:

• Quaternion-Valued Artificial Neural Networks (QVANNs) offer advanced computational capabilities.
• Synchronization and encryption are critical in secure data transmission.
• Existing QVANN models face challenges with complex equations and computational efficiency.

Purpose of the Study:

• To develop novel exponential synchronization and encryption techniques using QVANNs.
• To enhance computational efficiency in QVANNs through simplified complex equations.
• To demonstrate the practical application of QVANNs in secure color image processing.

Main Methods:

• Utilized the Cayley-Dickson representation for simplifying QVANN equations.
• Employed Lyapunov theorem to design a stable control system for synchronization.
• Conducted extensive numerical simulations to validate theoretical findings.

Main Results:

• Achieved efficient exponential synchronization in QVANNs.
• Demonstrated enhanced computational efficiency by exploiting complex number properties.
• Successfully applied QVANNs for encryption and decryption of color images.

Conclusions:

• The proposed QVANN model offers significant advancements in synchronization and encryption.
• The novel simplification techniques enhance computational efficiency and network stability.
• Findings pave the way for further research in complex artificial neural networks with diverse delay types.