The Prediction of Back Titration Based on Kernel Principal Component Analysis and Radial Basis Function Neural Network
Tiebin Wu, Wen Long, Yunlian Liu, Xinjun Li
Available Online July 2015.
- https://doi.org/10.2991/lemcs-15.2015.172How to use a DOI?
- Sampling method; KPCA ; RBF; IPSO; BT
- 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.
- 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 -