Authors: Sonia Alimi, Mohamed Chtourou
Addresses: Department of Electrical Engineering, National School of Engineers of Sfax, BP.W, Sfax 3038, Tunisia. ' Department of Electrical Engineering, National School of Engineers of Sfax, BP.W, Sfax 3038, Tunisia
Abstract: This paper presents an improved constructive learning algorithm for fuzzy inference system identification. An incremental training procedure that starts with a single pattern and a single-fuzzy rule has been used: if after several attempts, the fuzzy model cannot reduce the error within the specified tolerance; it grows by adding a new fuzzy rule. In order to overcome the over-training problem and to ameliorate the performance of the previous algorithm, two techniques of reduction have been introduced. In the first one, the growing of the fuzzy rules is conditioned by the generalisation error. In the second approach, a technique based on the similarity measures has been applied. The presented approaches have been applied for two examples to show the identification performance.
Keywords: constructive algorithms; fuzzy rule reduction; generalisation; similarity measures; fuzzy inference systems; FIS identification; constructive learning.
International Journal of Modelling, Identification and Control, 2007 Vol.2 No.4, pp.322 - 331
Published online: 28 Dec 2007 *Full-text access for editors Access for subscribers Purchase this article Comment on this article