NDLI: Identification of trash types and computation of trash content in ginned cotton using soft computing techniques

Content Provider	IEEE Xplore Digital Library
Author	Siddaiah, M. Prasad, N.R. Lieberman, M.A. Hughs, S.E.
Copyright Year	1999
Description	Author affiliation: Klipsch Sch. of Electr. & Comput. Eng., New Mexico State Univ., Las Cruces, NM, USA (Siddaiah, M.)
Abstract	This paper discusses the use of soft computing techniques such as Fuzzy Logic and Neural Network based approaches in the identification of various types of trash (non-lint material/foreign matter), and the computation of trash content in ginned cotton. Lint is the cotton fiber; non-lint or foreign matter is essentially everything other than lint. Trash content is the percentage of sample surface covered by non-lint particles. The effectiveness of a hybrid neurofuzzy structure, namely the Adaptive Network-Based Fuzzy Inference System (ANFIS) to classify trash types is compared with other techniques.
Starting Page	547
Ending Page	550
File Size	574391
Page Count	4
File Format	PDF
ISBN	0780354915
DOI	10.1109/MWSCAS.1999.867325
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1999-08-08
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Cotton US Department of Agriculture Computer networks Laboratories Neural networks Fuzzy neural networks Fuzzy systems Fuzzy logic Adaptive systems Textile industry
Content Type	Text
Resource Type	Article

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Identification of trash types and computation of trash content in ginned cotton using soft computing techniques