|Hughs, Sidney - Hughs ed|
Submitted to: Institute of Electrical and Electronics Engineers Proceedings Fuzzy Systems
Publication Type: Proceedings
Publication Acceptance Date: 4/28/1999
Publication Date: N/A
Citation: Interpretive Summary: Research at the Southwestern Cotton Ginning Research Laboratory (SWCGRL) has resulted in improving our ability to identify trash categories of bark1 (fibrous), bark2 (non-fibrous), leaf, and pepper in cotton. Work in the area of trash content computation and techniques to predict the trash content and compare with AMS measurements is currently ongoing.
Technical Abstract: The paper discusses the use of soft computing techniques such as neural networks and Fuzzy Logic based approaches in the identification of various trash (non-lint material/foreign matter) in ginned cotton. Lint is the cotton fiber; non-lint or foreign matter is everything other than lint. The effectiveness of a hybrid neuro-fuzzy structure, namely the Adaptive Network-Based Fuzzy Inference System (ANFIS) to classify trash types is compared to other techniques. Shape descriptors like shape factor, extent, and solidity measures are used to distinguish trash types.