| Title |
Structural Design of Radial Basis Function-based Polynomial Neural Networks by Using Multiobjective Particle Swarm Optimization |
| Authors |
김욱동(Kim, Wook-Dong) ; 오성권(Oh, Sung-Kwun) |
| DOI |
https://doi.org/10.5370/KIEE.2012.61.1.135 |
| Keywords |
Polynomial neural networks ; Radial basis function neural networks ; Fuzzy C-means multiobjective particle swarm optimization ; Pattern classifier |
| Abstract |
In this paper, we proposed a new architecture called radial basis function-based polynomial neural networks classifier that consists of heterogeneous neural networks such as radial basis function neural networks and polynomial neural networks. The underlying architecture of the proposed model equals to polynomial neural networks(PNNs) while polynomial neurons in PNNs are composed of Fuzzy-c means-based radial basis function neural networks(FCM-based RBFNNs) instead of the conventional polynomial function. We consider PNNs to find the optimal local models and use RBFNNs to cover the high dimensionality problems. Also, in the hidden layer of RBFNNs, FCM algorithm is used to produce some clusters based on the similarity of given dataset. The proposed model depends on some parameters such as the number of input variables in PNNs, the number of clusters and fuzzification coefficient in FCM and polynomial type in RBFNNs. A multiobjective particle swarm optimization using crowding distance (MoPSO-CD) is exploited in order to carry out both structural and parametric optimization of the proposed networks. MoPSO is introduced for not only the performance of model but also complexity and interpretability. The usefulness of the proposed model as a classifier is evaluated with the aid of some benchmark datasets such as iris and liver. |