Xinge Liu

According to our database, Xinge Liu authored at least 21 papers between 2016 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

Synchronization of fractional-order delayed coupled networks with reaction-diffusion terms and Neumann boundary value conditions.

Shuailei Zhang, Xinge Liu, Saeed Ullah, Meilan Tang, Hongfu Xu

Commun. Nonlinear Sci. Numer. Simul., February, 2024

An efficient construction of S-box based on the fractional-order Rabinovich-Fabrikant chaotic system.

Saeed Ullah, Xinge Liu, Adil Waheed, Shuailei Zhang

Integr., January, 2024

Mittag-Leffler stability and stabilization of delayed fractional-order memristive neural networks based on a new Razumikhin-type theorem.

Shuailei Zhang, Meilan Tang, Xinge Liu, Xian-Ming Zhang

J. Frankl. Inst., 2024

2023

Stability and stabilization of fractional-order non-autonomous systems with unbounded delay.

Shuailei Zhang, Meilan Tang, Xuemei Li, Xinge Liu

Commun. Nonlinear Sci. Numer. Simul., 2023

Catch my Eyebrow, Catch my Mind: Examining the Effect of Upper Facial Expressions on Emotional Recognition for VR Avatars.

Xin Yi, Ziyu Han, Xinge Liu, Yutong Ren, Xin Tong, Yan Kong, Hewu Li

Proceedings of the IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, 2023

2022

Improved results on stability and $$H_{\infty }$$ performance analysis for discrete-time neural networks with time-varying delay.

Qiao Chen, Xinge Liu, Peiyu Guo, Hua Liu, Xiayun Li

Comput. Appl. Math., July, 2022

Pseudo-State Estimation for Fractional Order Neural Networks.

Fengxian Wang, Xinge Liu

Neural Process. Lett., 2022

New criteria on the finite-time stability of fractional-order BAM neural networks with time delay.

Xuemei Li, Xinge Liu, Shuailei Zhang

Neural Comput. Appl., 2022

Further improved global exponential stability result for neural networks with time-varying delay.

Qiao Chen, Xinge Liu, Xuemei Li

Neural Comput. Appl., 2022

Finite-time synchronisation of delayed fractional-order coupled neural networks.

Shuailei Zhang, Xinge Liu, Xuemei Li

Int. J. Syst. Sci., 2022

Stability analysis of delayed neural network based on the convex method and the non-convex method.

Xiaofang Hu, Xinge Liu, Meilan Tang

Neurocomputing, 2022

Improved Results on L_{2-L_∞} State Estimation for Neural Networks with Time-varying Delay.

Qiao Chen, Xinge Liu, Fengxian Wang

Circuits Syst. Signal Process., 2022

2021

Asymptotic stability of static neural networks with interval time-varying delay based on LMI.

Meilan Tang, Xiaofang Hu, Xinge Liu, Qiao Chen

Neurocomputing, 2021

Asymptotical synchronization analysis of fractional-order complex neural networks with non-delayed and delayed couplings.

Li Li, Xinge Liu, Meilan Tang, Shuailei Zhang, Xian-Ming Zhang

Neurocomputing, 2021

Synchronization of a Riemann-Liouville Fractional Time-Delayed Neural Network with Two Inertial Terms.

Shuailei Zhang, Meilan Tang, Xinge Liu

Circuits Syst. Signal Process., 2021

2020

New Results on Stability for a Class of Fractional-Order Static Neural Networks.

Xiangqian Yao, Meilan Tang, Fengxian Wang, Zhijian Ye, Xinge Liu

Circuits Syst. Signal Process., 2020

2019

Further results on stability and synchronization of fractional-order Hopfield neural networks.

Feng-Xian Wang, Xinge Liu, Meilan Tang, Lifang Chen

Neurocomputing, 2019

2017

Improved exponential stability criterion for neural networks with time-varying delay.

Xiaofan Liu, Xinge Liu, Meilan Tang, Feng-Xian Wang

Neurocomputing, 2017

A new hybrid constructive neural network method for impacting and its application on tungsten price prediction.

Muzhou Hou, Liu Taohua, Yunlei Yang, Zhu Hao, Liu Hongjuan, Yuan Xiugui, Xinge Liu

Appl. Intell., 2017

Composite anti-disturbance autopilot design for missile system with angle constraint.

Xinge Liu, Haibin Sun, Jiayuan Shan

Proceedings of the IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society, Beijing, China, October 29, 2017

2016

Further results on exponential stability of delayed neural networks.

Xiaofan Liu, Xinge Liu, Meilan Tang

Proceedings of the 12th International Conference on Natural Computation, 2016