An Improved Odor Recognition System Using Learning Vector Quantization with a New Discriminant Analysis

نویسندگان

  • Turgay Temel
  • Bekir Karlik
چکیده

A high-performance biologically-inspired odor identification system is described. As a means of odor recognition, learning vector quantization (LVQ) algorithm is employed. Performance improvement is obtained with the use of a preprocessing with discriminant analysis of input samples. Due to sample-based decision, the system can be reliably operated as a real-time electronic nose.

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تاریخ انتشار 2009