A fuzzy neural network tree with heuristic backpropagation learning

Yan Qing Zhang; Fu Lai Korris Chung

A fuzzy neural network tree with heuristic backpropagation learning

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

4 Citations (Scopus)

Abstract

To solve the curse of dimensionality of a conventional fuzzy neural network, a fuzzy neural network tree based on the normal fuzzy reasoning is proposed. The heuristic backpropagation learning algorithm using a divide-and-conquer method is developed to enhance learning quality in term of discovered knowledge, training error and prediction error. Simulations have shown that the fuzzy neural network tree is able to discover meaningful fuzzy rules with low training errors and low prediction errors. In the future, the fuzzy neural network tree will have more applications in large-scale data mining and data fusion, machine learning, and eBusiness.

Original language	English
Title of host publication	Proceedings of the International Joint Conference on Neural Networks
Pages	553-558
Number of pages	6
Publication status	Published - 1 Jan 2002
Externally published	Yes
Event	2002 International Joint Conference on Neural Networks (IJCNN '02) - Honolulu, HI, United States Duration: 12 May 2002 → 17 May 2002

Conference

Conference	2002 International Joint Conference on Neural Networks (IJCNN '02)
Country/Territory	United States
City	Honolulu, HI
Period	12/05/02 → 17/05/02

ASJC Scopus subject areas

Software

Cite this

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title = "A fuzzy neural network tree with heuristic backpropagation learning",

abstract = "To solve the curse of dimensionality of a conventional fuzzy neural network, a fuzzy neural network tree based on the normal fuzzy reasoning is proposed. The heuristic backpropagation learning algorithm using a divide-and-conquer method is developed to enhance learning quality in term of discovered knowledge, training error and prediction error. Simulations have shown that the fuzzy neural network tree is able to discover meaningful fuzzy rules with low training errors and low prediction errors. In the future, the fuzzy neural network tree will have more applications in large-scale data mining and data fusion, machine learning, and eBusiness.",

author = "Zhang, {Yan Qing} and Chung, {Fu Lai Korris}",

year = "2002",

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AU - Chung, Fu Lai Korris

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N2 - To solve the curse of dimensionality of a conventional fuzzy neural network, a fuzzy neural network tree based on the normal fuzzy reasoning is proposed. The heuristic backpropagation learning algorithm using a divide-and-conquer method is developed to enhance learning quality in term of discovered knowledge, training error and prediction error. Simulations have shown that the fuzzy neural network tree is able to discover meaningful fuzzy rules with low training errors and low prediction errors. In the future, the fuzzy neural network tree will have more applications in large-scale data mining and data fusion, machine learning, and eBusiness.

AB - To solve the curse of dimensionality of a conventional fuzzy neural network, a fuzzy neural network tree based on the normal fuzzy reasoning is proposed. The heuristic backpropagation learning algorithm using a divide-and-conquer method is developed to enhance learning quality in term of discovered knowledge, training error and prediction error. Simulations have shown that the fuzzy neural network tree is able to discover meaningful fuzzy rules with low training errors and low prediction errors. In the future, the fuzzy neural network tree will have more applications in large-scale data mining and data fusion, machine learning, and eBusiness.

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M3 - Conference article published in proceeding or book

SP - 553

EP - 558

BT - Proceedings of the International Joint Conference on Neural Networks

T2 - 2002 International Joint Conference on Neural Networks (IJCNN '02)

Y2 - 12 May 2002 through 17 May 2002

ER -

A fuzzy neural network tree with heuristic backpropagation learning

Abstract

Conference

ASJC Scopus subject areas

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