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A Bio-Inspired Spiking Neural Network with Few-Shot Class-Incremental Learning for Gas Recognition.

Dexuan Huo¹, Jilin Zhang¹, Xinyu Dai¹

¹School of Integrated Circuits, Tsinghua University, Beijing 100084, China.

Sensors (Basel, Switzerland)

|March 11, 2023

Summary

This summary is machine-generated.

A new bio-inspired spiking neural network (SNN) effectively recognizes nine flammable and toxic gases. This model supports few-shot class-incremental learning, enabling rapid retraining for robust gas detection in real-world fire scenarios.

Keywords:

few-shot learning gas recognition incremental learning spiking neural network

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Area of Science:

Artificial Intelligence
Sensor Technology
Chemical Sensing

Background:

Gas sensor performance degrades due to drifting, aging, and environmental factors like temperature and humidity.
Maintaining gas recognition accuracy requires effective methods to counteract performance decline.
Incremental online learning offers a solution for adapting sensor networks to changing conditions.

Purpose of the Study:

To develop a bio-inspired spiking neural network (SNN) for accurate recognition of nine flammable and toxic gases.
To enable few-shot class-incremental learning for rapid retraining of the gas recognition system.
To validate the SNN's robustness and effectiveness compared to traditional algorithms in real-life fire scenarios.

Main Methods:

Development of a bio-inspired spiking neural network (SNN) architecture.
Implementation of few-shot class-incremental learning for online retraining capabilities.
Comparative analysis against Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Principal Component Analysis (PCA) + SVM, PCA + KNN, and Artificial Neural Network (ANN).

Main Results:

The proposed SNN achieved the highest accuracy of 98.75% in five-fold cross-validation for identifying nine gases across five concentrations.
The SNN demonstrated a 5.09% higher accuracy compared to other evaluated gas recognition algorithms.
The network's ability for rapid retraining with new gas types at a low accuracy cost was confirmed.

Conclusions:

The bio-inspired SNN offers a robust and effective solution for gas recognition, overcoming limitations of traditional methods.
The SNN's few-shot class-incremental learning capability ensures sustained performance in dynamic environments.
The developed SNN is highly suitable for real-life fire scenarios requiring reliable flammable and toxic gas detection.