2022 - Research.com Electronics and Electrical Engineering in South Korea Leader Award
The scientist’s investigation covers issues in Control theory, Artificial neural network, Linear matrix inequality, Control theory and Exponential stability. His Control theory study frequently involves adjacent topics like Stability criterion. His Artificial neural network research includes elements of Stability, Symmetric matrix, Interval and Synchronization.
His studies deal with areas such as Norm, Lyapunov stability and Convex optimization as well as Linear matrix inequality. His biological study deals with issues like Control, which deal with fields such as Markov jump. His biological study spans a wide range of topics, including Zero and State.
His main research concerns Control theory, Control theory, Nonlinear system, Artificial neural network and Exponential stability. His study in Control theory is interdisciplinary in nature, drawing from both Interval and Fuzzy logic. The Nonlinear system study combines topics in areas such as Function, Bounded function and Filter.
The concepts of his Artificial neural network study are interwoven with issues in Stability, State and Synchronization. His Exponential stability research focuses on Stability criterion and how it connects with Circle criterion. He studied Linear matrix inequality and Convex optimization that intersect with Mathematical optimization.
Ju H. Park focuses on Control theory, Nonlinear system, Control theory, Fuzzy logic and Artificial neural network. His study ties his expertise on Bounded function together with the subject of Control theory. His studies in Nonlinear system integrate themes in fields like Function, Multi-agent system and Fault tolerance.
In Control theory, he works on issues like Exponential stability, which are connected to Applied mathematics. The various areas that Ju H. Park examines in his Fuzzy logic study include Observer, Control system, Convex combination and Asynchronous communication. His Artificial neural network study combines topics from a wide range of disciplines, such as Memristor, Reaction–diffusion system and Synchronization.
Ju H. Park mainly focuses on Control theory, Control theory, Fuzzy logic, Nonlinear system and Fuzzy control system. Ju H. Park works on Control theory which deals in particular with Actuator. His work carried out in the field of Control theory brings together such families of science as Upper and lower bounds, State, Interval and Stability theory.
His research integrates issues of Observer, Convex combination, Discrete time and continuous time and Applied mathematics in his study of Fuzzy logic. His Nonlinear system study incorporates themes from Norm, Bounded function and Filter. His Artificial neural network research is multidisciplinary, relying on both Stability, Memristor and Synchronization.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Synchronization of Genesio chaotic system via backstepping approach
Ju H. Park.
Chaos Solitons & Fractals (2006)
Extended Dissipative State Estimation for Markov Jump Neural Networks With Unreliable Links
Hao Shen;Yanzheng Zhu;Lixian Zhang;Ju H. Park.
IEEE Transactions on Neural Networks (2017)
A novel criterion for delayed feedback control of time-delay chaotic systems
J.H. Park;O.M. Kwon.
Chaos Solitons & Fractals (2005)
Stability of time-delay systems via Wirtinger-based double integral inequality
MyeongJin Park;OhMin Kwon;Ju H. Park;SangMoon Lee.
Reliable mixed passive and ℋ∞ filtering for semi‐Markov jump systems with randomly occurring uncertainties and sensor failures
Hao Shen;Hao Shen;Zheng-Guang Wu;Ju H. Park.
International Journal of Robust and Nonlinear Control (2015)
Adaptive synchronization of hyperchaotic Chen system with uncertain parameters
Ju H. Park.
Chaos Solitons & Fractals (2005)
LMI optimization approach on stability for delayed neural networks of neutral-type
Ju H. Park;O. M. Kwon;Sang-Moon Lee.
Applied Mathematics and Computation (2008)
Robust extended dissipative control for sampled-data Markov jump systems
Hao Shen;Ju H. Park;Lixian Zhang;Zheng-Guang Wu.
International Journal of Control (2014)
Adaptive synchronization of fractional-order memristor-based neural networks with time delay
Haibo Bao;Haibo Bao;Ju H. Park;Jinde Cao;Jinde Cao.
Nonlinear Dynamics (2015)
Finite-time H∞ fuzzy control of nonlinear Markovian jump delayed systems with partly uncertain transition descriptions
Jun Cheng;Ju H. Park;Yajuan Liu;Zhijun Liu.
Fuzzy Sets and Systems (2017)
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below: