| Title |
Design of Type-2 Radial Basis Function Neural Networks Modeling for Sewage Treatment Process |
| Authors |
이승철(Lee, Seung-Cheol) ; 권학주(Kwun, Hak-Joo) ; 오성권(Oh, Sung-Kwun) |
| DOI |
https://doi.org/10.5370/KIEE.2015.64.10.1469 |
| Keywords |
Type-2 RBFNNs ; KM algorithm ; Back-propagation ; Sewage treatment process |
| Abstract |
In this paper, The methodology of Type-2 fuzzy set-based Radial Basis Function Neural Network(T2RBFNN) is proposed for Sewage Treatment Process and the simulator is developed for application to the real-world sewage treatment plant by using the proposed model. The proposed model has robust characteristic than conventional RBFNN. architecture of network consist of three layers such as input layer, hidden layer and output layer of RBFNN, and Type-2 fuzzy set is applied to receptive field in contrast with conventional radial basis function. In addition, the connection weights of the proposed model are defined as linear polynomial function, and then are learned through Back-Propagation(BP). Type reduction is carried out by using Karnik and Mendel(KM) algorithm between hidden layer and output layer. Sewage treatment data obtained from real-world sewage treatment plant is employed to evaluate performance of the proposed model, and their results are analyzed as well as compared with those of conventional RBFNN. |