Proceedings of the International Conference on Logistics, Engineering, Management and Computer Science

The Prediction of Back Titration Based on Kernel Principal Component Analysis and Radial Basis Function Neural Network

Authors
Tiebin Wu, Wen Long, Yunlian Liu, Xinjun Li
Corresponding Author
Tiebin Wu
Available Online July 2015.
DOI
https://doi.org/10.2991/lemcs-15.2015.172How to use a DOI?
Keywords
Sampling method; KPCA ; RBF; IPSO; BT
Abstract
A sampling method on the basis of imitation orthogonalization is proposed to ensure the typicality and ergodicity of samples. Considering the characteristics of back titration (BT) during cobalt removal with arsenic salt, such as many influencing factors and strong coupling, kernel principal component analysis (KPCA) is applied at first. Through KPCA, the effective characteristics of data can be extracted to reduce the dimensions of variables and to eliminate the coupling between variables. Then the extracted characteristic components are utilized as the input of radial basis function (RBF) neural network. Owing to there are many parameters in RBF neural network, which means that it is difficult to obtain the global optimal parameters, rival penalized competitive learning (RPCL) algorithm is adopted first to determine the original values of hidden nodes. On this basis, the improved particle swarm optimization (IPSO) is employed to select the parameters of RBF neural network. It is proved by the simulation results of industrial data that the BT prediction model is effective.
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Proceedings
International Conference on Logistics Engineering, Management and Computer Science (LEMCS 2015)
Part of series
Advances in Intelligent Systems Research
Publication Date
July 2015
ISBN
978-94-6252-102-5
ISSN
1951-6851
DOI
https://doi.org/10.2991/lemcs-15.2015.172How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Tiebin Wu
AU  - Wen Long
AU  - Yunlian Liu
AU  - Xinjun Li
PY  - 2015/07
DA  - 2015/07
TI  - The Prediction of Back Titration Based on Kernel Principal Component Analysis and Radial Basis Function Neural Network
BT  - International Conference on Logistics Engineering, Management and Computer Science (LEMCS 2015)
PB  - Atlantis Press
SP  - 870
EP  - 873
SN  - 1951-6851
UR  - https://doi.org/10.2991/lemcs-15.2015.172
DO  - https://doi.org/10.2991/lemcs-15.2015.172
ID  - Wu2015/07
ER  -