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25Years (2002-Present)
(Calculated once Scilit publication data is synced: current year − year of first publication + 1)
Representative Publications
View All Publications10 November 2022 in IEEE Transactions on Instrumentation and Measurement
Journal Article
In general, the goal of existing infrared and visible image fusion (IVIF) methods is to make the fused image contain both the high-contrast regions of the infrared image and the texture details of the visible image. However, this definition would lead the fusion image losing information from the visible image in high-contrast areas. For this problem, this paper proposed a decoupling network-based IVIF method (DNFusion), which utilizes the decoupled maps to design additional constraints on the network to force the network to retain the saliency information of the source image effectively. The current definition of image fusion is satisfied while effectively maintaining the saliency objective of the source images. Specifically, the feature interaction module inside effectively facilitates the information exchange within the encoder and improves the utilization of complementary information. Also, a hybrid loss function constructed with weight fidelity loss, gradient loss, and decoupling loss which ensures the fusion image to be generated to effectively preserves the source image’s texture details and luminance information. The qualitative and quantitative comparison of extensive experiments demonstrates that our model can generate a fused image containing saliency objects and clear details of the source images, and the method we proposed has a better performance than other state-of-the-art methods.
7 January 2020 in IEEE Transactions on Automatic Control
Journal Article
23 November 2022 in IEEE Transactions on Cybernetics
Journal Article
JWJing Wang
Jing WangJWJiacheng Wu
Jiacheng Wu
Hao Shen
Jinde Cao
Leszek RutkowskiIn this article, a novel hybrid reinforcement $Q$ -learning control method is proposed to solve the adaptive fuzzy $H_{\infty }$ control problem of discrete-time nonlinear Markov jump systems based on the Takagi–Sugeno fuzzy model. First, the core problem of adaptive fuzzy $H_{\infty }$ control is converted to solving fuzzy game coupled algebraic Riccati equation, which can hardly be solved by mathematical methods directly. To solve this problem, an offline parallel hybrid learning algorithm is first designed, where system dynamics should be known as a prior. Furthermore, an online parallel $Q$ -learning hybrid learning algorithm is developed. The main characteristics of the proposed online hybrid learning algorithms are threefold: 1) system dynamics are avoided during the learning process; 2) compared with the policy iteration method, the restriction of the initial stable control policy is removed; and 3) compared with the value iteration method, a faster convergence rate can be obtained. Finally, we provide a tunnel diode circuit system model to validate the effectiveness of the present learning algorithm.
7 March 2008 in IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)
Journal Article
In this paper, we propose and study a general array model of coupled delayed neural networks with hybrid coupling, which is composed of constant coupling, discrete-delay coupling, and distributed-delay coupling. Based on the Lyapunov functional method and Kronecker product properties, several sufficient conditions are established to ensure global exponential synchronization based on the design of the coupling matrices, the inner linking matrices, and/or some free matrices representing the relationships between the system matrices. The conditions are expressed within the framework of linear matrix inequalities, which can be easily computed by the interior-point method. In addition, a typical chaotic cellular neural network is used as the node in the array to illustrate the effectiveness and advantages of the theoretical results.
1 March 2006 in Chaos: An Interdisciplinary Journal of Nonlinear Science
Journal Article
Jinde CaoJLJianquan Lu
Jianquan Lu
In this paper, based on the invariant principle of functional differential equations, a simple, analytical, and rigorous adaptive feedback scheme is proposed for the synchronization of almost all kinds of coupled identical neural networks with time-varying delay, which can be chaotic, periodic, etc. We do not assume that the concrete values of the connection weight matrix and the delayed connection weight matrix are known. We show that two coupled identical neural networks with or without time-varying delay can achieve synchronization by enhancing the coupling strength dynamically. The update gain of coupling strength can be properly chosen to adjust the speed of achieving synchronization. Also, it is quite robust against the effect of noise and simple to implement in practice. In addition, numerical simulations are given to show the effectiveness of the proposed synchronization method.
About Me
Jinde Cao received the B.S. degree in mathematics from Anhui Normal University, Wuhu, China in 1986, the M.S. degree from Yunnan University, Kunming, China, and the Ph.D. degree from Sichuan University, Chengdu, China, both in applied mathematics, 1989, and 1998, respectively. He was a Postdoctoral Research Fellow at the Department of Automation and Computer-Aided Engineering, Chinese University of Hong Kong, Hong Kong, China from 2001 to 2002.
Professor Cao is an Endowed Chair Professor, the Dean of Science Department and the Director of the Research Center for Complex Systems and Network Sciences at Southeast University (SEU). He is also the Director of the National Center for Applied Mathematics at SEU-Jiangsu of China and the Director of the Jiangsu Provincial Key Laboratory of Networked Collective Intelligence of China. He is also Honorable Professor of Institute of Mathematics and Mathematical Modeling, Almaty, Kazakhstan.
Prof. Cao was a recipient of the National Innovation Award of China, Obada Prize and the Highly Cited Researcher Award in Engineering, Computer Science, and Mathematics by Clarivate Analytics. He was elected as a fellow of IEEE, a member of Russian Academy of Sciences, a member of the Academia Europaea (Academy of Europe), a member of Polish Academy of Sciences, a member of the European Academy of Sciences and Arts, a member of the Lithuanian Academy of Sciences, a fellow of African Academy of Sciences, and a fellow of Pakistan Academy of Sciences.