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An Incremental Learning Structure using Granular Computing and Model Fusion With Application to Materials Processing

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2 Author(s)
Panoutsos, G. ; Dept. of Autom. Control & Syst. Eng., Sheffield Univ. ; Mahfouf, M.

This paper introduces a neural-fuzzy (NF) modeling structure for offline incremental learning. Using a hybrid model updating algorithm (supervised/unsupervised) this NF structure has the ability to adapt in an additive way to new input-output mappings and new classes. Data granulation is utilised along with a NF structure to create a high performance yet transparent model that entails the core of the system. A model fusion approach is then employed to provide the incremental update of the system. The proposed system is tested against a multidimensional modeling environment consisting of a complex, non-linear and sparse database

Published in:

Intelligent Systems, 2006 3rd International IEEE Conference on

Date of Conference:

Sept. 2006