Influence of kernel clustering on an RBFN. Issue 4 (14th November 2019)

• Record Type: Journal Article
• Authors: Zhu, Changming; Miao, Duoqian
• Is Part Of: CAAI transactions on intelligence technology. Volume 4:Issue 4(2019)
• Page Start: 255
• Page End: 260
• Publication Date: 2019-11-14
• Subjects: radial basis function networks -- pattern clustering -- pattern classification; RBFN -- classical radial basis function network -- nonlinear separable data sets -- activation functions -- original kernel clustering -- different kernel‐clustering ways -- multiple kernel‐clustering criteria -- feasible clustering results -- bubble sort -- escape nearest outlier -- ENO -- pattern distribution; C5290 Neural computing techniques -- C6130 Data handling techniques; Artificial intelligence -- Periodicals; Computer science -- Periodicals; Artificial intelligence; Computer science; Electronic journals; Periodicals; 006.305
• DOI: 10.1049/trit.2019.0036
• Languages: English
• ISSNs: 2468-6557

Abstract

Abstract : Classical radial basis function network (RBFN) is widely used to process the non‐linear separable data sets with the introduction of activation functions. However, the setting of parameters for activation functions is random and the distribution of patterns is not taken into account. To process this issue, some scholars introduce the kernel clustering into the RBFN so that the clustering results are related to the parameters about activation functions. On the base of the original kernel clustering, this study further discusses the influence of kernel clustering on an RBFN when the setting of kernel clustering is changing. The changing involves different kernel‐clustering ways [bubble sort (BS) and escape nearest outlier (ENO)], multiple kernel‐clustering criteria (static and dynamic) etc. Experimental results validate that with the consideration of distribution of patterns and the changes of setting of kernel clustering, the performance of an RBFN is improved and is more feasible for corresponding data sets. Moreover, though BS always costs more time than ENO, it still brings more feasible clustering results. Furthermore, dynamic criterion always cost much more time than static one, but kernel number derived from dynamic criterion is fewer than the one from static.