S
Shun-Feng Su
Researcher at National Taiwan University of Science and Technology
Publications - 256
Citations - 5987
Shun-Feng Su is an academic researcher from National Taiwan University of Science and Technology. The author has contributed to research in topics: Fuzzy control system & Fuzzy logic. The author has an hindex of 35, co-authored 231 publications receiving 4358 citations. Previous affiliations of Shun-Feng Su include National Taiwan Normal University & Purdue University.
Papers
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Dynamic Event-Triggered Control for Interval Type-2 Fuzzy Systems Under Fading Channel
TL;DR: This article is to tackle the event-based state-feedback control problem for interval type-2 (IT2) fuzzy systems subject to the fading channel by taking the global membership boundary information into stability analysis and employing the membership-function-dependent analysis method.
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Radial Basis Function Networks With Linear Interval Regression Weights for Symbolic Interval Data
TL;DR: To handle symbolic interval data, the Gaussian functions required in the RBFNs are modified to consider interval distance measure, and the synaptic weights of the RB FNs are replaced by linear interval regression weights.
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Asynchronous Feedback Control for Delayed Fuzzy Degenerate Jump Systems Under Observer-Based Event-Driven Characteristic
TL;DR: This article is concerned with the issue of asynchronous feedback control for fuzzy degenerate jump systems with mode-dependent time-varying delays via Takagi–Sugeno fuzzy control technique under observer-based event-driven characteristic.
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Learning Error Feedback Design of Direct Adaptive Fuzzy Control Systems
Yao-Chu Hsueh,Shun-Feng Su +1 more
TL;DR: Based on the robust control approach proposed in the previous study, a way of estimating learning errors for direct adaptive fuzzy control systems can be derived and it can be found that such estimation is effective, as expected.
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Hybrid compensation control for affine TSK fuzzy control systems
TL;DR: The paper proposes a way of designing state feedback controllers for affine Takagi-Sugeno-Kang fuzzy models by combining two different control design methodologies, designed to compensate all rules so that the desired control performance can appear in the overall system.