Broad Learning System with Takagi-Sugeno Fuzzy Subsystem for Tobacco Origin Identification based on Near Infrared Spectroscopy

TL;DR

This paper introduces a rapid and accurate broad learning system with a Takagi-Sugeno fuzzy subsystem for tobacco origin identification using near infrared spectroscopy, significantly reducing training time compared to traditional neural networks.

Contribution

It proposes a novel broad learning system with TS fuzzy subsystem and incremental learning for fast, accurate tobacco origin identification from NIR data.

Findings

Achieved 95.59% prediction accuracy.

Training time reduced to about 3 seconds for incremental learning.

Outperformed traditional neural networks and deep learning models.

Abstract

Tobacco origin identification is significantly important in tobacco industry. Modeling analysis for sensor data with near infrared spectroscopy has become a popular method for rapid detection of internal features. However, for sensor data analysis using traditional artificial neural network or deep network models, the training process is extremely time-consuming. In this paper, a novel broad learning system with Takagi-Sugeno (TS) fuzzy subsystem is proposed for rapid identification of tobacco origin. Incremental learning is employed in the proposed method, which obtains the weight matrix of the network after a very small amount of computation, resulting in much shorter training time for the model, with only about 3 seconds for the extra step training. The experimental results show that the TS fuzzy subsystem can extract features from the near infrared data and effectively improve the recognition performance. The proposed method can achieve the highest prediction accuracy (95.59 %) in comparison to the traditional classification algorithms, artificial neural network, and deep convolutional neural network, and has a great advantage in the training time with only about 128 seconds.