Correlation-based incremental learning network for gas sensors drift compensation classification

Abstract

A gas sensor array is used for gas analysis to aid in an inspection. The signals from the sensor array are fed into machine learning models for learning and classification. These signals are characterized by time series fluctuating according to the environment or drift. When an unseen pattern is entered, the classification may be incorrect, resulting in decreased model performance. Creating a new model results in the problem of forgetting the old knowledge called Catastrophic Forgetting. Accordingly, this research proposes Correlation-Based Incremental Learning Network (CILN) using the Correlation Distance method to measure similarity and the Gaussian membership function to determine membership of each node. The gas sensor array data is used to verify the proposed algorithm by choosing 16 steady-state features (DR) from 13,910 records which are divided into 6 classes: 1) Ethanol, 2) Ethylene, 3) Ammonia, 4) Acetaldehyde, 5) Acetone, and 6) Toluene. The data are normalized and divided as the training sets into 10%, 20%, 30%, 40%, and 50%, respectively. The proposed algorithm was compared with well-known classifiers. CILN experiment results yield the highest accuracy of 98.96% using 50% of the training data set. It shows that CILN has the incremental learning ability and can be used with data that fluctuate according to the situation.