基本情况

1988年和1991年在陕77779193永利官网数学系分别获理学学士学位和计算数学硕士学位，2000年在西安电子科技大学应用数学系获理学博士学位; 应邀到香港浸会大学等院校从事神经计算的合作研究十余次。

主要从事最优化理论与算法、神经动力系统优化、演化计算的研究工作。提出了多种求解非线性规划和变分不等式问题的神经网络模型、多种解数值优化的智能方法，在IEEE Trans. Neural Netw. Learn. Syst (IEEE Trans. Neural Netw.), Information Sciences, Knowledge-Based System，Computers and Chemical Engineering, Swarm and Evolutionary Computation, Neural Computation, Neurocomputing, Applied Soft Computing等刊物和国际国内学术会议上发表学术论文60 余篇。 主持完成国家自然科学基金面上项目2项，省自然科学基础计划1项；主要参与国家及省基金6项。2016年获陕西高等学校科学技术奖一等奖。

代表性学术论文

• 1. Xingbao Gao, Exponential stability of globally projected dynamic systems. IEEE Trans. Neural Netw., 2003,14(2):426-43
  

• 2. Xingbao Gao, A novel neural network for nonlinear convex programming. IEEE Trans. Neural Netw., 2004, 15(3):613-621

• 3. Xingbao Gao, Li-Zhi Liao & Liqun Qi, A novel neural network for variational inequalities with linear and nonlinear constraints. IEEE Trans. Neural Netw., 2005, 16(6):1305-1317

• 4. Xingbao Gao & Li-Zhi Liao, A novel neural network for a class of convex quadratic minimax problems. Neural Computation, 2006, 18(8):1818-1846

• 5. Xingbao Gao & Li-Zhi Liao, A new projection-based neural network for constrained variational inequalities. IEEE Trans. Neural Netw., 2009, 20(3):373-388

• 6. Xingbao Gao & Li-Zhi Liao, A new one-layer neural network for linear and quadratic programming. IEEE Trans. Neural Netw., 2010, 21(6):918-929

• 7. Xingbao Gao & Li-Zhi Liao, Stability and convergence analysis for a class of neural networks. IEEE Trans. Neural Netw., 2011, 22(11):1770-1782

• 8. Xingbao Gao & Lizhi Liao, A novel neural network for generally constrained variational inequalities. IEEE Trans. Neural Netw. Learn. Syst., 2017, 28(9): 2062- 2075

• 9. Mengnan Tian & Xingbao Gao*, Differential evolution with neighborhood-based adaptive evolution mechanism for numerical optimization. Information Sciences, 2019, 478:422-448

• 10. Xingbao Gao & Lizhi Liao, Novel continuous- and discrete-time neural networks for solving quadratic minimax problems with linear equality constraints. IEEE Trans. Neural Netw. Learn. Syst., 2023. https://doi.org/10.1109/TNNLS.2023.3236695