Application of FCM Algorithm Combined with Artificial Neural Network in TBM Operation Data

• Published in: Computer modeling in engineering & sciences 2021-01, Vol.126 (1), p.397-417
• Main Authors: Fang, Jingyi, Song, Xueguan, Yao, Nianmin, Shi, Maolin
• Format: Article
• Language: English
• Subjects: Artificial Neural Network; Data Clustering; Fcm; Functional Data; Tbm
• ISSN: 1526-1492
• DOI: 10.32604/cmes.2021.012895

Fuzzy clustering theory is widely used in data mining of full-face tunnel boring machine. However, the traditional fuzzy clustering algorithm based on objective function is difficult to effectively cluster functional data. We propose a new Fuzzy clustering algorithm, namely FCM-ANN algorithm. The algorithm replaces the clustering prototype of the FCM algorithm with the predicted value of the artificial neural network. This makes the algorithm not only satisfy the clustering based on the traditional similarity criterion, but also can effectively cluster the functional data. In this paper, we first use the t-test as an evaluation index and apply the FCM-ANN algorithm to the synthetic datasets for validity testing. Then the algorithm is applied to TBM operation data and combined with the crossvalidation method to predict the tunneling speed. The predicted results are evaluated by RMSE and R2. According to the experimental results on the synthetic datasets, we obtain the relationship among the membership threshold, the number of samples, the number of attributes and the noise. Accordingly, the datasets can be effectively adjusted. Applying the FCM-ANN algorithm to the TBM operation data can accurately predict the tunneling speed. The FCM-ANN algorithm has improved the traditional fuzzy clustering algorithm, which can be used not only for the prediction of tunneling speed of TBM but also for clustering or prediction of other functional data.