Zobrazeno 1 - 10
of 31
pro vyhledávání: '"Zhen-Yao Chen"'
Autor:
Jung-Hua Wang, Te-Hua Hsu, Yi-Chung Lai, Yan-Tsung Peng, Zhen-Yao Chen, Ying-Ren Lin, Chang-Wen Huang, Chung-Ping Chiang
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-11 (2023)
Abstract Global warming and pollution could lead to the destruction of marine habitats and loss of species. The anomalous behavior of underwater creatures can be used as a biometer for assessing the health status of our ocean. Advances in behavior re
Externí odkaz:
https://doaj.org/article/7192bfb320be441e86352f778dc5be87
Autor:
Zhen-Yao Chen
Publikováno v:
International Journal of Computational Intelligence Systems, Vol 15, Iss 1, Pp 1-16 (2022)
Abstract This research attempts to reinforce the cultivating expression of radial basis function neural network (RBFnet) through computational intelligence (CI) and swarm intelligence (SI) learning methods. Consequently, the artificial immune system
Externí odkaz:
https://doaj.org/article/efbbcf139afa4491ac65a7cd6728d2cc
Autor:
Zhen-Yao Chen
Publikováno v:
Applied Artificial Intelligence, Vol 36, Iss 1 (2022)
This research attempts to enhance the learning performance of radial basis function neural network (RBFNuNet) via swarm intelligence (SI) and meta-heuristic algorithms (MHAs). Further, the genetic algorithm (GA) and ant colony optimization (ACO) algo
Externí odkaz:
https://doaj.org/article/1621ac3e33ec45739e4ab4bfcf8e55f4
Autor:
Zhen-Yao Chen
Publikováno v:
Applied Artificial Intelligence, Vol 39, Iss 1 (2025)
This paper strives to raise the imitating effectiveness of radial basis function-based neural network (RNNet) through biological learning (BL) and swarm intelligence (SI) optimization algorithms. Latter, the artificial immune system (AIS) and particl
Externí odkaz:
https://doaj.org/article/f1edc9b3a250476090cc56c3f672d358
Autor:
Zhen-Yao Chen, 陳振耀
98
Forecasting is one of the crucial factors in practical application since it ensures the effective allocation of capacity and proper amount of inventory. Since auto-regressive integrated moving average (ARIMA) models which are more suitable fo
Forecasting is one of the crucial factors in practical application since it ensures the effective allocation of capacity and proper amount of inventory. Since auto-regressive integrated moving average (ARIMA) models which are more suitable fo
Externí odkaz:
http://ndltd.ncl.edu.tw/handle/d83vya
Autor:
Zhen-Yao Chen
Publikováno v:
Applied Artificial Intelligence. 36
This research attempts to enhance the learning performance of radial basis function neural network (RBFNuNet) via swarm intelligence (SI) and meta-heuristic algorithms (MHAs). Further, the genetic ...
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b573096932c638d7c2504d0c8399f7ae
https://doi.org/10.1080/08839514.2021.1994217
https://doi.org/10.1080/08839514.2021.1994217
Publikováno v:
Frontiers in Cell and Developmental Biology, Vol 9 (2021)
Frontiers in Cell and Developmental Biology
Frontiers in Cell and Developmental Biology
Background: Gastrointestinal Cancer (GICs) is the most common group of malignancies, and many of its types are the leading causes of cancer related death worldwide. Pseudogenes have been revealed to have critical regulatory roles in human cancers. Th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2ba430fc9c391c2d04117be2fdb321a9
https://pubmed.ncbi.nlm.nih.gov/34660601
https://pubmed.ncbi.nlm.nih.gov/34660601
Autor:
Zhen-Yao Chen
Publikováno v:
Advances in Computational Collective Intelligence ISBN: 9783030881122
ICCCI (CCIS Volume)
ICCCI (CCIS Volume)
This paper attempts to improve the learning representation of radial basis function neural network (RBFNN) through metaheuristic algorithm (MHA) and evolutionary algorithm (EA). Next, the ant colony optimization (ACO)-based and genetic algorithm (GA)
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1938864856cacdbd3b10c336d8948d34
https://doi.org/10.1007/978-3-030-88113-9_4
https://doi.org/10.1007/978-3-030-88113-9_4
Autor:
R. J. Kuo, Zhen-Yao Chen
Publikováno v:
Journal of Intelligent Manufacturing. 30:1137-1154
This paper intends to enhance the learning performance of radial basis function neural network (RBFnn) using self-organizing map (SOM) neural network (SOMnn). In addition, the particle swarm optimization (PSO) and genetic algorithm (GA) based (PG) al
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ef83f33caab7ed6e78920eff732766eb
https://doi.org/10.1007/s10845-017-1313-7
https://doi.org/10.1007/s10845-017-1313-7