A fuzzy neural network tree with heuristic backpropagation learning

Author

Zhang, Yan-Qing ; Chung, Fu-lai

Author_Institution

Dept. of Comput. Sci., Georgia State Univ., Atlanta, GA, USA

Volume

1

fYear

2002

fDate

6/24/1905 12:00:00 AM

Firstpage

553

Lastpage

558

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 e-business

Keywords

backpropagation; fuzzy logic; fuzzy neural nets; inference mechanisms; neural net architecture; data fusion; discovered knowledge; divide-and-conquer method; e-business; fuzzy neural network tree; fuzzy reasoning; heuristic backpropagation learning; large-scale data mining; learning quality; local forward-wave learning algorithm; machine learning; meaningful fuzzy rules; prediction error; training error; Backpropagation; Computer networks; Computer science; Fuzzy neural networks; Fuzzy reasoning; Fuzzy sets; Fuzzy systems; Input variables; Neural networks; Neurons;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on

Conference_Location

Honolulu, HI

ISSN

1098-7576

Print_ISBN

0-7803-7278-6

Type

conf

DOI

10.1109/IJCNN.2002.1005532

Filename

1005532