DocumentCode :
2204852
Title :
Recursive training for multi-resolution fuzzy min-max neural network classifier
Author :
Xi, Chen ; Dongming, Jin ; Zhijian, Li
Author_Institution :
Inst. of Microelectron., Tsinghua Univ., Beijing, China
Volume :
1
fYear :
2001
fDate :
2001
Firstpage :
131
Abstract :
A new training algorithm for the Fuzzy Min-Max Neural Network (FMMNN) is proposed. The FMMNN model is a powerful tool for pattern classification problems, and is perfect for hardware implementation. But the original model has several unwilling properties. Among them a serious one is how to decide the crucial training parameters. This paper proposes a recursive training algorithm to alleviate the difficulty, and improves the training procedure highly automatic. The result model is a multi-resolution combined classifier (MRCC). Experiments are made following some recent evaluation criteria known in literature, and show that compared with the original model, the MRCC has better classification performance, better adaptive learning ability and consume less computation resource
Keywords :
fuzzy neural nets; learning (artificial intelligence); minimax techniques; pattern classification; adaptive learning ability; fuzzy min-max neural network classifier; highly automatic training; hyperbox fuzzy sets; hyperbox membership function; learning machine; low computation resource; multiresolution combined classifier; pattern classification; recursive training algorithm; stopping conditions; Character recognition; Fuzzy neural networks; Fuzzy sets; Fuzzy systems; Hardware; Input variables; Microelectronics; Neural networks; Optimization methods; Pattern classification;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Solid-State and Integrated-Circuit Technology, 2001. Proceedings. 6th International Conference on
Conference_Location :
Shanghai
Print_ISBN :
0-7803-6520-8
Type :
conf
DOI :
10.1109/ICSICT.2001.981440
Filename :
981440
Link To Document :
بازگشت