Zobrazeno 1 - 10
of 1 054
pro vyhledávání: '"Li, Jiangang"'
Memory, as the basis of learning, determines the storage, update and forgetting of knowledge and further determines the efficiency of learning. Featured with the mechanism of memory, a radial basis function neural network based learning control schem
Externí odkaz:
http://arxiv.org/abs/2308.04223
In radial basis function neural network (RBFNN) based real-time learning tasks, forgetting mechanisms are widely used such that the neural network can keep its sensitivity to new data. However, with forgetting mechanisms, some useful knowledge will g
Externí odkaz:
http://arxiv.org/abs/2211.07909
Nuclear fusion represents one of the best alternatives for a sustainable source of clean energy. Tokamaks allow to confine fusion plasma with magnetic fields and one of the main challenges in the control of the magnetic configuration is the predictio
Externí odkaz:
http://arxiv.org/abs/2207.05695
In recent years, machine learning (ML) research methods have received increasing attention in the tokamak community. The conventional database (i.e., MDSplus for tokamak) of experimental data has been designed for small group consumption and is mainl
Externí odkaz:
http://arxiv.org/abs/2206.08414
Autor:
SUN, Yang, LIU, Hong, PENG, Junwei, SHEN, Minchong, HU, Yang, YU, Dongsheng, LI, Jiangang *, DONG, Yuanhua
Publikováno v:
In Pedosphere June 2024 34(3):553-566
Publikováno v:
In Ecological Indicators May 2024 162
Autor:
Chao, Zhang, Wang, Xiaojie, Wu, Dajun, Tang, Yunying, Wang, Hanlin, Li, Dingzhen, Liu, Fukun, Wu, Muquan, Yan, Peiguang, Gao, Xiang, Li, Jiangang
Publikováno v:
In Nuclear Engineering and Technology May 2024 56(5):1619-1626
Publikováno v:
Zhongguo linchuang yanjiu, Vol 36, Iss 9, Pp 1286-1290 (2023)
Objective To investigate the effects of microRNA-21 (miR-21) on proliferation, apoptosis and aerobic clycolysis of pancreatic cancer cells and its possible mechanism. Methods Human pancreatic cancer cell SW1990 and normal pancreatic duct epithelial c
Externí odkaz:
https://doaj.org/article/079d219b1d514e7cbf73f2c04e3bad6a
In this work, a purely data-driven discharge prediction model was developed and tested without integrating any data or results from simulations. The model was developed based on the experimental data from the Experimental Advanced Superconducting Tok
Externí odkaz:
http://arxiv.org/abs/2110.00346