A fuzzy neural network tree with heuristic backpropagation learning

Yan Qing Zhang, Fu Lai Korris Chung

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-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 languageEnglish
Title of host publicationProceedings of the International Joint Conference on Neural Networks
Pages553-558
Number of pages6
Publication statusPublished - 1 Jan 2002
Externally publishedYes
Event2002 International Joint Conference on Neural Networks (IJCNN '02) - Honolulu, HI, United States
Duration: 12 May 200217 May 2002

Conference

Conference2002 International Joint Conference on Neural Networks (IJCNN '02)
Country/TerritoryUnited States
CityHonolulu, HI
Period12/05/0217/05/02

ASJC Scopus subject areas

  • Software

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