Electronic nose recognition method based on bionic olfactory bulb model and convolutional neural network

Abstract

The invention relates to an electronic nose recognition method based on a bionic olfactory bulb model and a convolutional neural network. The electronic nose recognition method comprises: sampling to-be-recognized object by using an electronic nose platform to obtain an electronic nose sample data set S; constructing a bionic olfactory bulb model, wherein the bionic olfactory bulb model is formedby connecting a plurality of olfactory glomerulus models, the number of the olfactory glomerulus models in the bionic olfactory bulb model is the same as the number of electronic nose sensors, each olfactory glomerulus model is formed by connecting four basic neuron models, and the four basic neuron models respectively are an olfactory receptor, a mitral cell, a granulosa cell and a olfactory glomerulus pericyte; inputting the ample data set S into the bionic olfactory bulb model by using the olfactory receptor, and processing to obtain a new multivariate pulse time series data set S'; carrying out data normalization processing; obtaining a corresponding grayscale data set M; determining a convolutional neural network model; and training. With the electronic nose recognition method of thepresent invention, the automatic feature extraction and the end-to-end learning can be achieved, and the versatility of the electronic nose recognition algorithm can be improved.

Description

technical field [0001] The invention belongs to the field of instruments and measurement, in particular to an electronic nose recognition method based on a bionic olfactory bulb model and a convolutional neural network. Background technique [0002] The electronic nose is a modern bionic detection instrument, which can simulate the structure and function of the human and mammalian olfactory system, and realize the detection and recognition of simple or complex odors. The recognition of smell by electronic nose is a typical pattern recognition problem, and the recognition methods currently used are all classical pattern recognition methods. Electronic nose sampling signals usually need to go through data analysis steps such as data preprocessing, feature extraction, feature dimensionality reduction and classifier identification, and finally obtain the recognition results of different odors. This recognition method based on the classic pattern recognition idea has a mature al...

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Problems solved by technology

[0003] However, this idea also has shortcomings: 1) Data preprocessing, feature extraction, feature dimensionality reduction and classifier design, each step has many optional algorithms / methods, and for different electronic nose systems and applications, often It is necessary to try a large number of different algorithm combinations to obtain the "optimal" recognition effect, especially the electronic nose feature extraction step, and there are no guidelines for this algorithm combination trial process, so it is very time-consuming; 2) Different electronic noses Systems and applications often require different algorithm combinations to obtain better recognition results, which means that the algorithm lacks versatility, which is not conducive to the promotion of electronic nose technology in different applications

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