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A New Approach to Generate A Self-Organizing Fuzzy Neural Network Model

Biomedical Sciences Research Institute Computer Science Research Institute Environmental Sciences Research Institute Nanotechnology & Advanced Materials Research Institute

Leng, G, Prasad, G and McGinnity, TM (2002) A New Approach to Generate A Self-Organizing Fuzzy Neural Network Model. In: 2002 IEEE International Conference on Systems, Man, and Cybernetics, Tunisia. IEEE. 6 pp. [Conference contribution]

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URL: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1173311&tag=1

DOI: 10.1109/ICSMC.2002.1173311

Abstract

This paper presents a new approach for creating a self-organizing fuzzy neural network (SOFNN) from training data, to implement the Takagi-Sugeno-Kang (TSK) model. The center vector and the width vector have been introduced in the RBF neurons in the SOFNN. Novel methods of structure learning and parameter learning, based on new adding and pruning techniques and a recursive on-line learning algorithm, are proposed and developed. The proposed methods are very simple and effective and generate a fuzzy neural model with a high accuracy and a very compact structure. Simulation studies based on a pH neutralization process, confirm that the SOFNN has the capability of self-organization, and can determine the structure and parameters of the network automatically without non-linear optimization.

Item Type:Conference contribution (Paper)
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Intelligent Systems
Research Institutes and Groups:Computer Science Research Institute
Computer Science Research Institute > Intelligent Systems Research Centre
ID Code:18492
Deposited By:Professor Girijesh Prasad
Deposited On:16 May 2011 11:50
Last Modified:20 May 2011 15:24

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