Broad Learning System with Takagi-Sugeno Fuzzy Subsystem for Tobacco Origin Identification based on Near Infrared Spectroscopy
This work addresses rapid identification for the tobacco industry, but it is incremental as it builds on existing broad learning and fuzzy systems.
The paper tackled the problem of time-consuming training in tobacco origin identification using near infrared spectroscopy by proposing a broad learning system with a Takagi-Sugeno fuzzy subsystem, achieving a prediction accuracy of 95.59% and reducing training time to about 128 seconds.
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.