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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Citations (Scopus)

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.
LanguageEnglish
Title of host publicationUnknown Host Publication
Number of pages6
DOIs
Publication statusPublished - Oct 2002
Event2002 IEEE International Conference on Systems, Man, and Cybernetics, Tunisia -
Duration: 1 Oct 2002 → …

Conference

Conference2002 IEEE International Conference on Systems, Man, and Cybernetics, Tunisia
Period1/10/02 → …

Fingerprint

Fuzzy neural networks
Learning algorithms
Neurons

Cite this

@inproceedings{51aa5a742c3449d1865eee73e5be425b,
title = "A New Approach to Generate A Self-Organizing Fuzzy Neural Network Model",
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.",
author = "G Leng and G Prasad and TM McGinnity",
year = "2002",
month = "10",
doi = "10.1109/ICSMC.2002.1173311",
language = "English",
booktitle = "Unknown Host Publication",

}

Leng, G, Prasad, G & McGinnity, TM 2002, A New Approach to Generate A Self-Organizing Fuzzy Neural Network Model. in Unknown Host Publication. 2002 IEEE International Conference on Systems, Man, and Cybernetics, Tunisia, 1/10/02. https://doi.org/10.1109/ICSMC.2002.1173311

A New Approach to Generate A Self-Organizing Fuzzy Neural Network Model. / Leng, G; Prasad, G; McGinnity, TM.

Unknown Host Publication. 2002.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AU - Prasad, G

AU - McGinnity, TM

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AB - 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.

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DO - 10.1109/ICSMC.2002.1173311

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