International Journal of Computational Intelligence Research (IJCIR)

Volume 3, Number 1 (2007)


Hierarchical and interpretable connectionist structure generation from data

Waratt Rattasiri, Saman K. Halgamuge
Dynamic Systems and Control, Department of Mechanical and Manufacturing Engineering, The University of Melbourne, Victoria 3010, Australia.

Nalin Wickramarachchi
Department of Electrical Engineering, University of Moratuwa, Moratuwa, Katubadda, Sri Lanka


In this paper, a self-generating hierarchical fuzzy system, namely Hierarchical Neuro-Fuzzy System (HiNeFS), is presented. While Hierarchical Neuro-Fuzzy System relies on the structure of the initial training network, it utilizes the newly proposed analytical criterions to identify the qualified rule relevant nodes based on their history of activities and behaviors which reflect their levels of involvement and contribution during the learning process. The proposed criterions have proved to be able to effectively reduce the size of the network, hence the computational complexity, and the resulting hierarchical network has demonstrated its capability in efficiently performing its task. To verify the proposed system performance, it is tested against two well-known benchmark datasets whose results are provided and discussed.

Neuro-fuzzy systems, hierarchical neuro-fuzzy systems, classification, pattern recognition.