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

DOI

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.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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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  - Proceedings of the International Conference on Logistics, Engineering, Management and Computer Science
PB  - Atlantis Press
SP  - 870
EP  - 873
SN  - 1951-6851
UR  - https://doi.org/10.2991/lemcs-15.2015.172
DO  - 10.2991/lemcs-15.2015.172
ID  - Wu2015/07
ER  -