| Biography | |
|---|---|
 Prof. Ziqian Liu State University of New York Maritime College, USA |
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| Title: New Optimal Control Strategy for Chaotic Synchronization of Coupled Noisy Neural Networks | |
| Abstract: Since the model of chaotic neural networks was developed three decades ago, the control of chaotic neural networks has been an area of intense research. With people now eager to build a powerful brain-like “neuromorphic” computer, the synchronization of coupled chaotic neural networks has become one of the hottest research topics in the arena of neural networks. Many research works have recently been reported in the literature. By studying those previous research results, we found out that most authors ignore the noise process when building their dynamic network models. However, it is well known that the noise process is always affiliated with signal transmission specially in the very-large-scale integration of electronic circuits and systems as a result that Moore’s law will end soon. Due to the presence of the disturbance as a part of reality, a real system is usually influenced by both internal and external random perturbations that are also called as noises. With respect to neural networks, the noise is an unavoidable factor that should be taken into consideration during the implementation of artificial neural networks. Therefore, in the past few years, the study of chaotic noisy neural networks has started to attract the attention from the research community. In this talk, we will present a novel optimal control strategy for chaotic synchronization of coupled noisy neural networks. The main objective of this design is to guarantee that the chaotic drive network synchronizes with the chaotic response network influenced by heavy noise signals. Especially, with respect to noise attenuation, the design achieves a prescribed level of noise debilitation with stability margins. In addition, the presented approach is simple to implement in real applications. It is our desire that the analytical results given by this talk could provide some useful design insights to accelerate the development of a brain-like “neuromorphic” computer. | |
| Biography: Ziqian Liu is currently a faculty member in the Department of Electrical Engineering, State University of New York Maritime College, USA. He received his Ph.D. degree from the Southern Illinois University Carbondale in 2005. From 2005 to 2008, he worked in Ingersoll- Rand Co. Ltd, USA. He joined State University of New York Maritime College in 2008. He was a faculty member in the Department of Electrical Engineering, Hefei University of Technology, China from 1989 to 1999. His current research interests are in developing mathematic models and controls for stochastic complex networks and brain-like computing. | |
