Incremental FCM Technique for Black Tea Quality Evaluation Using an Electronic Nose

被引：8
|
作者：
Tudu, B. [1 ]
Ghosh, S. [2 ]
Bag, A. K. [3 ]
Ghosh, D. [4 ]
Bhattacharyya, N. [4 ]
Bandyopadhyay, R. [1 ]
机构：
[1] Jadavpur Univ, Dept Instrumentat & Elect Engn, Kolkata 700098, India
[2] CSIR Cent Glass & Ceram Res Inst, Sensor & Actuator Div, 196 Raja SC Mullick Rd, Kolkata 700098, India
[3] FIEM, Dept Appl Elect & Instrumentat Engn, Kolkata 700150, India
[4] Ctr Dev Adv Comp, Kolkata 700091, India
来源：
FUZZY INFORMATION AND ENGINEERING | 2015年 / 7卷 / 03期
关键词：
Electronic nose; Black tea quality; Taster scores; Gas sensors; Fuzzy clustering; Incremental learning; Fuzzy c-means;
D O I：
10.1016/j.fiae.2015.09.002
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
A novel incremental algorithm based on fuzzy-c-means (FCM) method is proposed and implemented to effectively cluster data obtained from an electronic nose for black tea quality evaluation. The algorithm segregates data generated with the electronic nose from different batches of black tea into clusters with similar features, without requiring to access previously collected data. This feature of appending information exclusively from fresh data points entitles the algorithm to overcome catastrophic interference phenomenon common to conventional pattern recognition techniques.
引用
收藏
页码：275 / 289
页数：15
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