Zobrazeno 1 - 10
of 565
pro vyhledávání: '"Wang, Dianhui"'
Autor:
Wang, Dianhui, Dang, Gang
This paper presents a novel neuro-fuzzy model, termed fuzzy recurrent stochastic configuration networks (F-RSCNs), for industrial data analytics. Unlike the original recurrent stochastic configuration network (RSCN), the proposed F-RSCN is constructe
Externí odkaz:
http://arxiv.org/abs/2407.11038
Autor:
Wang, Dianhui, Dang, Gang
Temporal data modelling techniques with neural networks are useful in many domain applications, including time-series forecasting and control engineering. This paper aims at developing a recurrent version of stochastic configuration networks (RSCNs)
Externí odkaz:
http://arxiv.org/abs/2406.16959
Autor:
Felicetti, Matthew J., Wang, Dianhui
Neural networks for industrial applications generally have additional constraints such as response speed, memory size and power usage. Randomized learners can address some of these issues. However, hardware solutions can provide better resource reduc
Externí odkaz:
http://arxiv.org/abs/2310.19225
Autor:
Wang, Dianhui, Felicetti, Matthew J.
Real-time predictive modelling with desired accuracy is highly expected in industrial artificial intelligence (IAI), where neural networks play a key role. Neural networks in IAI require powerful, high-performance computing devices to operate a large
Externí odkaz:
http://arxiv.org/abs/2308.13570
Autor:
Wu, Chenxi, Lai, Huajun, Wang, Feng, Wang, Dianhui, Gan, Weijiang, She, Yulai, Wang, Zhongmin
Publikováno v:
In Journal of Alloys and Compounds 30 August 2024 997
Publikováno v:
In Information Sciences August 2024 677
Autor:
Felicetti, Matthew J., Wang, Dianhui
Publikováno v:
In Information Sciences August 2024 677
Publikováno v:
In Information Sciences August 2024 676
Autor:
Li, Junqi, Wang, Dianhui
Publikováno v:
In Knowledge-Based Systems 27 September 2024 300