Zobrazeno 1 - 10
of 16
pro vyhledávání: '"Chuandong Qin"'
Autor:
Chuandong Qin, Baole Han
Publikováno v:
IEEE Access, Vol 10, Pp 129322-129343 (2022)
computational efficiency of the quantum particle swarm optimization (QPSO) is significantly higher than that of the traditional particle swarm optimization when solving the parameters of the optimization problem model. However, the exploration and ex
Externí odkaz:
https://doaj.org/article/5f7c1075d5a8476c9ccaffff30edb549
Autor:
Baosheng Li, Chuandong Qin
Publikováno v:
IEEE Access, Vol 9, Pp 66531-66541 (2021)
Octane number is the most important indicator of reflecting the combustion performance, and a great deal of research has been devoted to improving it. In this paper, a new analytical framework is proposed to predict octane number, kernel principal co
Externí odkaz:
https://doaj.org/article/5cb8356afc2b4d928de17f2a6a7c9e2b
Publikováno v:
Computers, Materials & Continua; 2024, Vol. 79 Issue 2, p1975-1994, 20p
Autor:
Chuandong Qin, Baole Han
Publikováno v:
Computer Modeling in Engineering & Sciences. 136:3097-3119
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9cc7e3beeac91f9bef1f6f66bf3a7e13
https://doi.org/10.32604/cmes.2023.026643
https://doi.org/10.32604/cmes.2023.026643
Publikováno v:
The Journal of Supercomputing. 78:2265-2286
Traditional classification algorithms work well on general small-scale microarray datasets, but for large-scale scenarios, general machines are not capable of supporting the operation of these algorithms anymore for the memory and time costs. In this
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e66d44b82e0cdd592e16819ccc298e08
https://doi.org/10.1007/s11227-021-03962-7
https://doi.org/10.1007/s11227-021-03962-7
Publikováno v:
Engineering Applications of Artificial Intelligence. 120:105816
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c8e5baa5cc2a078e22cd70206a365806
https://doi.org/10.1016/j.engappai.2022.105816
https://doi.org/10.1016/j.engappai.2022.105816
Autor:
Chuandong Qin, Lingling Huang
Publikováno v:
International Journal of Machine Learning and Cybernetics. 10:2993-3002
The directing orbits of chaotic systems is a common multimodal optimization problem in the engineering field. However, when this multimodal optimization problem is solved by evolutionary algorithm, it is difficult for the method to obtain the high-qu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fe5f3910031c95b66c0172573b29d5d2
https://doi.org/10.1007/s13042-018-00917-y
https://doi.org/10.1007/s13042-018-00917-y
Publikováno v:
Neural Computing and Applications. 32:4709-4732
It is known that the existing $$\nu$$-twin support vector regression ($$\nu$$-TWSVR) has the ability to optimize $$\varepsilon _1$$ and $$\varepsilon _2$$ automatically through the proper selections of the parameters $$\nu _1$$ and $$\nu _2$$. Howeve
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2d6e85d3786d444a46aeea997c74d947
https://doi.org/10.1007/s00521-018-3823-4
https://doi.org/10.1007/s00521-018-3823-4
Publikováno v:
Applied Intelligence. 48:4023-4046
After combining the ν-Twin Support Vector Regression (ν-TWSVR) with the rough set theory, we propose an efficient Rough ν-Twin Support Vector Regression, called Rough ν-TWSVR for short. We construct a pair of optimization problems which are motiv
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8df7ba1bc745eff11e8a9fcf17a833ef
https://doi.org/10.1007/s10489-018-1185-3
https://doi.org/10.1007/s10489-018-1185-3
Publikováno v:
JUCS-Journal of Universal Computer Science 23(7): 603-618
Taking full advantages of the L1-norm support vector machine and the L2-norm support vector machine, a new improved double regularization support vector machine is proposed to analyze the datasets with small samples, high dimensions and high correlat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::44baa689aa4e85bb798c2a77c5907c51
https://lib.jucs.org/article/23363/
https://lib.jucs.org/article/23363/