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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Robust TSK fuzzy modeling for function approximation with outliers
TL;DR: A clustering algorithm termed as robust fuzzy regression agglomeration (RFRA) is proposed to define fuzzy subspaces in a fuzzy regression manner with robust capability against outliers and shows superior performance over other approaches.
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Credit assigned CMAC and its application to online learning robust controllers
TL;DR: This paper presents a learning robust controller that can actually learn online, and uses previous control input, current output acceleration, and current desired output as the state to define the nominal effective moment of the system from the CMAC table.
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Robust static output-feedback stabilization for nonlinear discrete-time systems with time delay via fuzzy control approach
TL;DR: This paper addresses the problem of designing robust static output-feedback controllers for nonlinear discrete-time interval systems with time delays both in states and in control input by derived in terms of the matrix spectral norm of the closed-loop fuzzy system.
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Dynamic Slip-Ratio Estimation and Control of Antilock Braking Systems Using an Observer-Based Direct Adaptive Fuzzy–Neural Controller
TL;DR: Simulation results of an ABS with the road estimator and the DAFC, which are shown to provide good effectiveness under varying road conditions.
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Environmental sound classification using a regularized deep convolutional neural network with data augmentation
Zohaib Mushtaq,Shun-Feng Su +1 more
TL;DR: The performance evaluation illustrates that the best accuracy attained by the proposed DCNN without max-pooling function (Model-2) and using Log-Mel audio feature extraction on those augmented datasets can accomplish the best performance on environment sound classification problems.