Prof. Quanxin Zhu（Link）
Xiaoxiang Scholar at Hunan Normal University, China
Brief introduction
Zhu has committed to the research of Markov process, stochastic system stability and control theory and application. So far he has mand a series of important progress and solved multiple open questions raised by international publications such as SIAM J. and Control Optim. He has published 130 SCI-indexed papers in international journals such as Automatica, IEEE TAC, IEEE TNN, IEEE TSMC (Part B), IEEE TNNLS and Syst. Control Lett. His papers have been cited more than 2,200 times by other SCII-indexed papers. His article published in SCI-index journal Nonlinear Anal.: RWA was rated as one of the 25 most popular articles in the Science Direct database in 2012. Wang was awarded as one of the Highly-Cited Scientists in 2018, Outstanding Reviewers for the Journal of the Franklin Institute in 2017 and 2018, for the Fuzzy Sets and Systems in 2018 (Independent), Neurocomputing in 2018, Applied Mathematics and Computation in 2018, Chaos, Solitons and Fractals in 2018, ISA Transactions in 2018. He serves as an editorial board member of 5 international publications, the guest editors for 4 special issues of international publications.
Speech Title: Recent Advances and Related Topics on the Stability of Stochastic Nonlinear Systems
Abstract: As is well-known, the research on stochastic nonlinear systems is an important topic in the field of stochastic control. In recent decades, stochastic nonlinear systems have been applied to many fields of mathematics, physics, biology, engineering, finance, and economics，etc. With the development of national economy and society, the actual system is becoming more and more complex. Moreover, the stability is often destroyed by earthquake, tsunami, war, financial crisis, etc. So the stability is one of the best challenging topics in the field of stochastic nonlinear systems. In this talk, we first introduce the model and background of stochastic nonlinear systems. Then, we give some definitions and classical results on stochastic stability. Finally, we present our recent results and methods on this topic.