A self-organizing map-based online correction sample generation method for artificial olfactory system

Abstract

The invention belongs to the technical field of odor analysis, and discloses a self-organizing map-based artificial olfactory system online correction sample generation method, including an initial training stage and an online update stage; the initial training stage uses the number of sample categories to construct a self-organizing map neural network. Layer structure, initialize the neuron weights of the self-organizing graph neural network with training samples and use the neuron weights of the self-organizing graph neural network as the initial training sample set; in the online update stage, according to the classification results of the subsequent classifiers, use the test samples to Local area neuron weights are adjusted. The self-organizing map neural network neuron weight at this time is used as an online training sample set for online correction of the pattern recognition method. The results show that the invention can improve the long-term drift resistance of the artificial olfactory system under online working conditions; it can automatically generate calibration samples during the online working process, which provides a guarantee for the automatic online calibration of the artificial olfactory system.

Description

technical field [0001] The invention belongs to the technical field of odor analysis, in particular to a self-organizing map-based method for generating online calibration samples for an artificial olfactory system. Background technique [0002] The artificial olfactory system is a novel method of odor analysis, which has the advantages of rapid detection, non-invasive, easy operation, and low cost. The artificial olfactory system is mainly divided into two parts: a gas sensor array and a pattern recognition method. The "gas sensor array" uses low-cost gas sensors with cross sensitivity to obtain odor maps; the "pattern recognition" uses artificial intelligence, machine learning, etc. Methods Qualitative and quantitative analysis of the odor. The "long-term drift" of the gas sensor is an unavoidable problem in the artificial olfactory system. With the prolongation of the use time, the odor spectrum of the "gas sensor array" will change slowly and irregularly; making the rec...

AI Technical Summary

Problems solved by technology

[0003] To sum up, the problems existing in the existing technology are: 1) For equipment that needs to work online for a long time, routine calibration cannot be performed, so the system recognition accuracy will drop significantly with the working time; 2) Conventional calibration methods need to prepare specific calibration samples And the equipment cannot work normally during the calibration period, wasting a lot of financial, manpower and material resources

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