NDLI: A Dual-Momentum Hybrid Wavelet Neural Net (DM-HWNN): Its performance evaluation and application

Content Provider	IEEE Xplore Digital Library
Author	Yi Zhi Zhao Xiang Li
Copyright Year	2009
Description	Author affiliation: Singapore Institute of Manufacturing Technology, 71 Nanyang Drive, Singapore 638075 (Yi Zhi Zhao; Xiang Li)
Abstract	This paper presents a novel single neural net-based classifier called Dual-Momentum Hybrid Wavelet Neural Nets (DM-HWNN). DM-HWNN inherits capability in learning efficiency from Wavelet Neural Networks (WNN) and performance consistency in classification from Back-Propagation Networks (BPN). An extra momentum term is introduced into the learning process to further speed up the convergence of the learning. K-fold cross validation (CV) over four benchmark datasets are conducted to compare the performance of this single neural net classifier with some existing multiple classifier systems (MCS) including Logiboost Bayesian Classifier (LBC), Multistage Neural Networks Ensemble (MNNE), and Self-Organizing Neural Grove (SONG). The results show that DM-HWNN outperforms the first three methods in term of classification accuracy and the SONG in term of computation time. Furthermore, a cutter dataset from industry milling machine is used to evidence classification capability of DM-HWNN and illustrate how DM-HWNN can be used in prediction of cutter's wear out.
Starting Page	325
Ending Page	330
File Size	279382
Page Count	6
File Format	PDF
ISBN	9781424437597
ISSN	19354576
DOI	10.1109/INDIN.2009.5195825
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-06-23
Publisher Place	UK
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Backpropagation Bayesian methods Neural networks Pulp manufacturing Boosting Prediction algorithms Data mining Bagging Metalworking machines Convergence
Content Type	Text
Resource Type	Article

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