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Incorporating approximate dynamic programming-based parameter tuning into PD-type virtual inertia control of DFIGs.

, , , and . IJCNN, page 1-8. IEEE, (2013)

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Approximate dynamic programming based supplementary reactive power control for DFIG wind farm to enhance power system stability., , , , , and . Neurocomputing, (2015)Cyclical Kernel Adaptive Metropolis., , and . CoRR, (2022)Ranking with Slot Constraints., , , and . CoRR, (2023)Using Recurrent Neural Network Structure and Multi-Head Attention with Convolution for Fraudulent Phone Text Recognition., , , , , and . Comput. Syst. Sci. Eng., 46 (2): 2277-2297 (2023)Error bound analysis of policy iteration based approximate dynamic programming for deterministic discrete-time nonlinear systems., , , , and . IJCNN, page 1-8. IEEE, (2015)Node-Aligned Graph-to-Graph Generation for Retrosynthesis Prediction., , , , , and . CoRR, (2023)Poli-see: An Interactive Tool for Visualizing Privacy Policies., , and . WPES@CCS, page 57-71. ACM, (2020)Policy Approximation in Policy Iteration Approximate Dynamic Programming for Discrete-Time Nonlinear Systems., , , and . IEEE Trans. Neural Networks Learn. Syst., 29 (7): 2794-2807 (2018)Online Supplementary ADP Learning Controller Design and Application to Power System Frequency Control With Large-Scale Wind Energy Integration., , , , , and . IEEE Trans. Neural Networks Learn. Syst., 27 (8): 1748-1761 (2016)A Qualitative Study of REST API Design and Specification Practices., , , and . VL/HCC, page 148-156. IEEE, (2023)