Online identification of a neuro-fuzzy model through indirect fuzzy clustering of data space
Ahmad Kalhor, Caro Lucas · 2009
In this paper, we propose a new approach to identify a neuro-fuzzy model. In our approach, data space is partitioned indirectly through a fuzzy clustering method. The clusters are not created directly through spatial features of data points. A gradient vector is defined as major feature of clustering in data space. This feature is estimated for each incoming data points. Creating and updating fuzzy membership functions, adding new clusters and removing redundant clusters are performed through it. Correspond with cluster parameters, fuzzy rules are defined and a neuro-fuzzy model is identified recursively. Prediction of monthly sunspots number is considered to demonstrate the capability of the proposed neuro-fuzzy model.