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.
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Efficiently solving general weapon-target assignment problem by genetic algorithms with greedy eugenics
TL;DR: The proposed algorithm is to enhance the performance of GAs by introducing a greedy reformation scheme so as to have locally optimal offspring and has the best performance when compared to other existing search algorithms.
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An immunity-based ant colony optimization algorithm for solving weapon–target assignment problem
TL;DR: An immunity-based ant colony optimization (ACO) algorithm for solving weapon–target assignment (WTA) problems is proposed and from the simulation for those WTA problems, the proposed algorithm indeed is very efficient.
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Adaptive Fuzzy Control With High-Order Barrier Lyapunov Functions for High-Order Uncertain Nonlinear Systems With Full-State Constraints
TL;DR: A high-order tan-type barrier Lyapunov function (BLF) is constructed to handle the full-state constraints of the control systems and by the BLF and combining a backstepping design technique, an adding a power integrator, and a fuzzy control, the proposed approach can control high- order uncertain nonlinear system with full- state constraints.
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Genetic algorithm with ant colony optimization (GA-ACO) for multiple sequence alignment
TL;DR: The proposed GA-ACO algorithm is to enhance the performance of genetic algorithm by incorporating local search, ant colony optimization (ACO), for multiple sequence alignment and has superior performance when compared to other existing algorithms.
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Robust support vector regression networks for function approximation with outliers
TL;DR: A novel regression approach, termed as the robust support vector regression (RSVR) network, is proposed to enhance the robust capability of SVR and shows that even the training lasted for a long period, the testing errors would not go up and the overfitting phenomenon is indeed suppressed.