An incremental adaptive neuro-fuzzy networks
Keun-Chang Kwak · 2008
In this paper, we propose a method for constructing an incremental adaptive neuro-fuzzy network (IANFN). In contrast to typical rule-based systems, the underlying principle is to consider a two-step development of adaptive neuro-fuzzy network (ANFN). First, we build a standard linear regression (LR) model which could be treated as a preliminary design capturing the linear part of the data. Next, all modeling discrepancies are compensated by a collection of rules that become attached to the regions of the input space in which the error becomes localized. The incremental network is constructed by building a collection of information granules through some specialized fuzzy clustering, called context-based fuzzy c-means (CFCM) that is guided by the distribution of error of the linear part of its development. The experimental results reveal that the proposed incremental network shows a good approximation and generalization capability in comparison with the general method.