The control of chaotic neural networks has been a significant research focus since their introduction three decades ago. With the growing interest in developing brain-like “neuromorphic” computing systems, synchronizing coupled chaotic neural networks has become a critical challenge. Most existing models, however, overlook the influence of noise - an inevitable factor in real-world systems, especially in the context of very-large-scale integration (VLSI) circuits and systems as Moore's law nears its limit. Noise, both internal and external, plays a vital role in signal transmission and must be accounted for in the implementation of artificial neural networks. 

 

This talk introduces a novel optimal control strategy aimed at synchronizing chaotic drive and response networks under heavy noise conditions. The proposed approach guarantees robust synchronization while achieving a specified level of noise attenuation, ensuring stability even in highly noisy environments. Not only is the method effective, but it is also simple to implement in practical applications. These analytical insights have the potential to accelerate the development of neuromorphic computing systems by offering a more realistic and resilient framework for chaotic neural network synchronization. 

Ziqian Liu is a Full Professor in the Department of Electrical Engineering at the State University of New York Maritime College, USA. He earned his Ph.D. from Southern Illinois University Carbondale in 2005. From 2005 to 2008, he worked with Ingersoll-Rand Co. Ltd. in the USA before joining SUNY Maritime College in 2008. Previously, from 1989 to 1999, he was a faculty member in the Department of Electrical Engineering at Hefei University of Technology, China. 
 
Dr. Liu’s research has been widely published in prestigious journals and conferences, including Neural Networks, IEEE Transactions on Systems, Man, and Cybernetics, and Optimal Control Applications and Methods. His research interests focus on chaotic synchronization control, memristive neural networks, coupled neural networks, intelligent control systems, stochastic optimal control, and stochastic complex networks.