Self-organizing map based artificial olfactory system online correction sample generation method

A technology for artificial smell and sample correction, applied in neural learning methods, biological neural network models, instruments, etc., to solve problems such as inability to perform routine calibration, decline in system recognition accuracy, and failure of equipment to work properly during calibration

Active Publication Date: 2017-09-29
CHONGQING UNIV
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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 significantl...

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  • Self-organizing map based artificial olfactory system online correction sample generation method
  • Self-organizing map based artificial olfactory system online correction sample generation method
  • Self-organizing map based artificial olfactory system online correction sample generation method

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Embodiment Construction

[0035] In order to make the object, technical solution and advantages of the present invention more clear, the present invention will be further described in detail below in conjunction with the examples. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0036] The application principle of the present invention will be described in detail below in conjunction with the accompanying drawings.

[0037] Such as figure 1 As shown, the self-organizing map-based artificial olfactory system online correction sample generation method provided by the embodiment of the present invention includes the following steps:

[0038] S101: Each detection is expressed as a vector, constructing a self-organizing graph neural network, and training samples enter the neural network for initialization;

[0039] S102: After completing the initial training, enter the test, and the subsequent classif...

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Abstract

The invention belongs to the technical field of smell analysis, and discloses a self-organizing map based artificial olfactory system online correction sample generation method. The method comprises an initial training stage and an online updating stage. In the initial training stage, a self-organizing map neural network multi-layer structure is constructed according to the number of sample categories, self-organizing map neural network neuron weight initialization is performed according to training samples, the self-organizing map neural network neuron weights are enabled to act as an initial training sample set; and in the online updating stage, and adjustment is performed on local region neuron weights by using a test sample according to a classification result of a follow-up classifier. The current self-organizing map neural network neuron weights are enabled to act as an online training sample set to perform online correction on a mode recognition method. Results show that the method can improve the long-term drift resisting capacity of an artificial olfactory system under an online working condition, can automatically generate a correction sample in the online working process and provides guarantee for the artificial olfactory system to realize automatic online correction.

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...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06N3/08G01N33/00
CPCG06N3/082G01N33/0001G06F18/24143G06F18/214
Inventor 刘涛李东琦陈建军武萌雅陈艳兵
Owner CHONGQING UNIV
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