Embedded electronic nose test system and test method based on saw sensor

Abstract

The invention discloses an SAW (Surface Acoustic Wave) sensor based embedded electronic nose testing system and testing method. The testing method comprises the steps of: (1) obtaining relevant training data and test data by a microcontroller; (2) if the format of the obtained data does not conform to a specification, processing; otherwise, skipping this step; (2) if a parameter setting signal is received, skipping to a step (6); otherwise, setting relevant parameters automatically; (4) adjusting relevant parameters according rules of neural networks until the training is finished; (5) if the training result reaches to requirements, skipping to a step (8); otherwise, continuing; (6) setting relevant parameters according to the reference setting signal until the training is finished; (7) if the training result reaches to the requirements, skipping to the step (8); otherwise, skipping back to the step (6); and (8) using the training result to mode identification or software measurement and obtaining practical output according to test data. According to the SAW sensor based embedded electronic nose testing system and testing method, disclosed by the invention, neural network algorithm is simplified and optimized, and the training process of the neural network can be transplanted in an embedded platform.

Description

technical field [0001] The invention relates to a test system for gas qualitative and quantitative analysis, in particular to an embedded wireless electronic nose test system based on a SAW (surface acoustic wave) sensor. Background technique [0002] An electronic nose is an electronic system that uses the response patterns of a gas sensor array to identify odors. It consists of a sensor array and an appropriate pattern recognition system that can identify simple or complex gases. A single sensor in an electronic nose is non-specific in response, producing a broad-spectrum response to a variety of gases. Due to the inevitable shortcomings of the sensor itself. The single parameter measurement sensor will cause great interference when measuring the mixed gas, and the measurement error is difficult to control. An effective way to improve the anti-interference ability is to use combined or arrayed multi-sensors and intelligent algorithms such as neural networks to achieve th...

AI Technical Summary

Problems solved by technology

The neural network algorithm has a large amount of computation and is complex. In order to achieve the characteristics of portability and miniaturization, the algorithm needs to be simplified and processed.

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