An improved odor recognition system using learning vector quantization with a new discriminant analysis

Title:

An improved odor recognition system using learning vector quantization with a new discriminant analysis

Creator:

Temel, Turgay and Karlik, Bekir

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:d96686b9-f25a-4909-82b1-573befb2aecf
uuid:d96686b9-f25a-4909-82b1-573befb2aecf

Subject:

Olfactory system, classification, odor recognition, pre-processing, and learning vector quantization

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

A new pre-processing algorithm for improved discrimination of odor samples is proposed. The pre-processed odor sample outputs from two sensors are input using a learning-vector quantization (LVQ) classifier as a means of odor recognition to be employed within electronic nose applications. The proposed algorithm brings out highly scattered classes while minimizing the within-class scatter of the samples given an odor class. LVQ is observed to operate robustly and reliably in terms of variation of parameters of interest, mainly a learning parameter. Due to the increased performance along with computational simplicity and robustness, the scheme is suitable to sample-by-sample identification of olfactory sensory data and can be easily adapted to hierarchical processing with other sensory data in real-time.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2007 Volume:17 | Number:4

Harvested from:

CDK

Metadata only:

false