Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Shi-Ku Wang"'
Autor:
Zeng-Shun Zhao, Xiang Feng, Yan-yan Lin, Fang Wei, Shi-Ku Wang, Tong-Lu Xiao, Mao-Yong Cao, Zeng-Guang Hou, Min Tan
Publikováno v:
Journal of Applied Mathematics, Vol 2013 (2013)
The Rao-Blackwellized particle filter (RBPF) algorithm usually has better performance than the traditional particle filter (PF) by utilizing conditional dependency relationships between parts of the state variables to estimate. By doing so, RBPF coul
Externí odkaz:
https://doaj.org/article/9c43a5dbf61144168f0f5a7c794dc2db
Autor:
Tong-Lu Xiao, Fang Wei, Zeng-Guang Hou, Zeng-Shun Zhao, Shi-Ku Wang, Maoyong Cao, Yan-yan Lin, Xiang Feng
Publikováno v:
Neurocomputing. 149:29-38
The neural network ensemble (NNE) is a very effective way to obtain a good prediction performance by combining the outputs of several independently trained neural networks. Swarm intelligence is applied here to model the population of interacting age
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e5fc187385f700e94902cf97e30b60aa
https://doi.org/10.1016/j.neucom.2013.12.062
https://doi.org/10.1016/j.neucom.2013.12.062
Autor:
Min Tan, Maoyong Cao, Fang Wei, Zeng-Shun Zhao, Zeng-Guang Hou, Shi-Ku Wang, Tong-Lu Xiao, Yan-yan Lin, Xiang Feng
Publikováno v:
J. Appl. Math.
Journal of Applied Mathematics, Vol 2013 (2013)
Journal of Applied Mathematics, Vol 2013 (2013)
The Rao-Blackwellized particle filter (RBPF) algorithm usually has better performance than the traditional particle filter (PF) by utilizing conditional dependency relationships between parts of the state variables to estimate. By doing so, RBPF coul
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::19e71cc8f0c325d93249c1c365ce8440
https://projecteuclid.org/euclid.jam/1394808109
https://projecteuclid.org/euclid.jam/1394808109
Publikováno v:
Advances in Neural Networks – ISNN 2013 ISBN: 9783642390647
ISNN (1)
ISNN (1)
The Neural-Network Ensemble (NNE) is a very effective method where the outputs of separately trained neural networks are combined to perform the prediction. In this paper, we introduce the improved Neural Network Ensemble (INNE) in which each compone
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::92376191d023c0efcf3649faab803697
https://doi.org/10.1007/978-3-642-39065-4_45
https://doi.org/10.1007/978-3-642-39065-4_45