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Machine olfaction odor distinguishing method based on modularized composite neural net

A neural network and machine smell technology, applied in the direction of instruments, simulation processes of specific applications, analysis materials, etc., can solve the problems of imbalanced training samples, no specific solutions, etc., to accelerate the learning speed, reduce the possibility, and facilitate the promotion. effect of ability

Inactive Publication Date: 2005-11-23
EAST CHINA UNIV OF SCI & TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0010] It is a natural way to convert an n-type problem into n two-type problems, but it will bring problems such as training sample imbalance
The invention does not specifically address how to implement computer data processing

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  • Machine olfaction odor distinguishing method based on modularized composite neural net
  • Machine olfaction odor distinguishing method based on modularized composite neural net
  • Machine olfaction odor distinguishing method based on modularized composite neural net

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Embodiment Construction

[0044] Below in conjunction with accompanying drawing, the present invention is described in further detail:

[0045] The olfactory simulation device on which the present invention is based is shown in FIG. 2 . The present invention solves the technical problem to be solved by the computer in the virtual frame shown in FIG. 1 .

[0046] According to the above-mentioned machine olfactory device, the method for judging the odor category and estimating the intensity, as shown in Figure 2, includes the following steps:

[0047] a. Place 30ml of the liquid or solid sample to be tested in a 250ml sample bottle, and incubate at 45±0.1°C for 30min.

[0048] b. The operator inserts the balanced one-way valve of the sample bottle into the air inlet on the test box, and the one-way valve opens. The micro-diaphragm pump sucks the volatile gas of the aroma substance in the headspace of the sample bottle into the sealed gas pipeline at a flow rate of 1.0 l / min. At this time, the odor to b...

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Abstract

A machine-aided smell identification method based on modularized combination neural net characterized by that, the combination neural net categorizer comprises a forward direction recessive single layer perceptron module and a RBF neural net module, wherein each categorizer module includes a plurality of sub-modules, whose arrangement is determined through the two phases of accretion and trimming, the parameters of center, width and weight value can be determined through error reversion algorithm. The invention solves the problem of the high dimension multiple catalog mass sample set in a fast and effective way. The machine-aided device by the invention can be used to distinguish many kinds of smells and to estimate the odor intensity simultaneously.

Description

technical field [0001] The invention relates to a modular combination neural network classifier for high-dimensional (≥60), large sample (≥60,000), and multi-category (≥1,000) problems and its application in a machine olfactory device. The modular combination classification The device gives a machine-olfactory device the ability to recognize thousands of odors and estimate their strength. Background technique [0002] At the International Academic Conference on Chemical Perception held by the North Atlantic Treaty Organization (NATO) in 1989, the academic community defined machine smell (also known as electronic nose) as: machine smell is an instrument composed of multiple gas sensors with overlapping performance. Composed of an appropriate pattern recognition method, it has the ability to recognize simple or complex odors. It is said that well-trained professionals can recognize 4,000 kinds of odors. In contrast, the recognition ability of machine...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01N27/04G01N27/12G01N35/00G06G7/60
Inventor 高大启
Owner EAST CHINA UNIV OF SCI & TECH