2012 - IEEE Fellow For contributions to theory and biomedical applications of fuzzy control
Hao Ying spends much of his time researching Control theory, Fuzzy logic, Fuzzy control system, Defuzzification and Fuzzy number. His studies in Control theory integrate themes in fields like Event, Fuzzy set, Automotive engineering, System identification and Sequence. Fuzzy logic is the subject of his research, which falls under Artificial intelligence.
Hao Ying interconnects PID controller and Nonlinear system in the investigation of issues within Fuzzy control system. His Defuzzification research is multidisciplinary, incorporating perspectives in Fuzzy set operations, Fuzzy classification, Fuzzy associative matrix and Algorithm. His study explores the link between Fuzzy number and topics such as Mathematical optimization that cross with problems in Fuzzy measure theory.
His main research concerns Fuzzy logic, Control theory, Fuzzy control system, Defuzzification and Fuzzy number. Hao Ying works on Fuzzy logic which deals in particular with Fuzzy set. His work on Adaptive neuro fuzzy inference system is typically connected to Mean arterial pressure as part of general Fuzzy control system study, connecting several disciplines of science.
He combines subjects such as Fuzzy set operations and Fuzzy associative matrix with his study of Defuzzification. His Fuzzy set operations research is multidisciplinary, incorporating elements of Neuro-fuzzy and Fuzzy classification. His study in Fuzzy number is interdisciplinary in nature, drawing from both Mathematical optimization and Applied mathematics.
The scientist’s investigation covers issues in Internal medicine, Endocrinology, Cell biology, Fuzzy logic and Skeletal muscle. His Fuzzy logic research includes themes of Event and Control theory. His Control theory study focuses on Control theory in particular.
His research investigates the link between Fuzzy control system and topics such as Mathematical optimization that cross with problems in Lyapunov function. The study incorporates disciplines such as Neuro-fuzzy, Machine learning and Fuzzy classification in addition to Fuzzy number. His work on Fuzzy set operations and Defuzzification as part of general Fuzzy set study is frequently connected to Gaussian, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
Hao Ying focuses on Cell biology, Endocrinology, Internal medicine, Signal transduction and Skeletal muscle. His Cell biology study integrates concerns from other disciplines, such as Programmed cell death and Adipose tissue, Thermogenin, Brown adipose tissue, Thermogenesis. Hao Ying performs integrative study on Endocrinology and KLF9 in his works.
His research integrates issues of Wallerian degeneration and SARM1 Gene in his study of Internal medicine. The various areas that Hao Ying examines in his Signal transduction study include Fibrosis, Skeletal muscle fibrosis, Myocyte and microRNA. In his study, Hormone is strongly linked to Stem cell, which falls under the umbrella field of microRNA.
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.
Fuzzy control theory: a nonlinear case
Hao Ying;William Siler;James J. Buckley.
Fuzzy Control and Modeling: Analytical Foundations and Applications
Apparatus and method for noninvasive doppler ultrasound-guided real-time control of tissue damage in thermal therapy
Hao Ying;Craig J. Hartley.
General SISO Takagi-Sugeno fuzzy systems with linear rule consequent are universal approximators
IEEE Transactions on Fuzzy Systems (1998)
Sufficient conditions on general fuzzy systems as function approximators
Sufficient conditions on uniform approximation of multivariate functions by general Takagi-Sugeno fuzzy systems with linear rule consequent
systems man and cybernetics (1998)
Modeling and control of fuzzy discrete event systems
Feng Lin;Hao Ying.
systems man and cybernetics (2002)
Introduction to Type-2 Fuzzy Logic Control: Theory and Applications
Jerry Mendel;Hani Hagras;Woei-Wan Tan;William W. Melek.
The simplest fuzzy controllers using different inference methods are different nonlinear proportional-integral controllers with variable gains
Practical design of nonlinear fuzzy controllers with stability analysis for regulating processes with unknown mathematical models
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: