NDLI: A fuzzy data-driven and rule-based reasoning system for setting the nano- particle milling process parameters

Content Provider	IEEE Xplore Digital Library
Author	Tung-Hsu Hou Chi-Hung Su
Copyright Year	2007
Description	Author affiliation: Nat. Yunlin Univ. of Sci. & Technol., Yunlin (Tung-Hsu Hou)
Abstract	In this research, an integration of adaptive resonance theory (ART-2) neural networks, fuzzy set theory, variable precision rough set (VPRS), decision tree C5.0 algorithm and fuzzy rule-based control is proposed to develop a fuzzy rule-based reasoning system to set a nano- particle milling process. The characteristics of the proposed system are to use data-driven to do fuzzification and rule extraction instead of directly using domain experts. In addition, the regulation scheme of the parameter setting system is based on adjusting the process parameters by using its current deviation from the optimal parameters. The verified results show that the proposed process parameter setting system indeed can be applied to guide engineers to set the process parameters when a nano-particle milling process is shifted. It can be applied to adjust the milling process from current shift condition back to near-optimal condition.
Starting Page	582
Ending Page	586
File Size	593877
Page Count	5
File Format	PDF
ISBN	9781424415281
DOI	10.1109/IEEM.2007.4419256
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-12-02
Publisher Place	Singapore
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	data mining fuzzy rulebased reasoning Adaptive control Fuzzy sets Fuzzy control Programmable control Milling Fuzzy neural networks Fuzzy set theory Resonance nano-particle milling process Fuzzy reasoning Fuzzy systems
Content Type	Text
Resource Type	Article

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