Abstract
By means of fuzzy c-means clustering algorithm, we propose a neuro-fuzzy learning algorithm for tunning fuzzy rules. In this new approach, firstly, we abstract so-called typical data from given training data by using fuzzy c-means clustering algorithm (FCM) in order to remove the redundant data and resolve conflicts in data whcih are considered disadvantages for the learning time and the convergence, and make them as practical training data. Then, according to these typical data, we tune fuzzy rules based on the neuro-fuzzy learning algorithm proposed by authors. These typical data created by FCM have similar characters and properties with the original training data esentially, and the number of the data is less than the original one, so that the learning time can be expected to be reduced. Also, it will be considered that the fuzzy rules generated by the combination of FCM and neuro-fuzzy technique are more reasonable and suitable for identifying a system than the case of using only the neuro-fuzzy learning algorithm. Moreover, the efficiency of the proposed approach is illustrated by numerical examples.
