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Xiaofeng Yang
Researcher at Fudan University
Publications - 37
Citations - 321
Xiaofeng Yang is an academic researcher from Fudan University. The author has contributed to research in topics: Computer science & Iterative learning control. The author has an hindex of 6, co-authored 26 publications receiving 117 citations. Previous affiliations of Xiaofeng Yang include Xidian University.
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Dynamic opposite learning enhanced teaching–learning-based optimization
TL;DR: Comprehensive numerical results with the comparisons with the state-of-the-art counterparts show that DOLTLBO has significant advantages of converging to the global optimum on most benchmarks and engineering problems, which also validates the superiority of the novel DOL operator.
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Iterative Learning Identification and Compensation of Space-Periodic Disturbance in PMLSM Systems With Time Delay
TL;DR: A novel iterative learning identification method that utilizes the partial but most pertinent information in the error signal is proposed to identify the force ripple in permanent-magnet linear synchronous motor (PMLSM) systems.
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Data-Driven Iterative Feedforward Tuning for a Wafer Stage: A High-Order Approach Based on Instrumental Variables
TL;DR: A novel data-driven feedforward tuning method that utilizes error data from all past iterations via an integrator in the learning law, yet without the need of the plant model or the sensitivity function is developed in the presence of noise.
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A two-stage model for rate-dependent inverse hysteresis in reluctance actuators
TL;DR: In this paper, a two-stage model for rate-dependent inverse hysteresis is proposed, which can directly model the inversion of ratedependent hystresis regardless of the hysteinis loop is symmetric or not.
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An enhanced teaching-learning-based optimization algorithm with self-adaptive and learning operators and its search bias towards origin
TL;DR: The comparative results of different dimensional problems indicate that the proposed SHSLTLBO has the best convergence and stability in solving all 28 benchmarks with low dimension and shifted solutions.