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- A manufacturing quality prediction model based on AdaBoost-LSTM with rough knowledgePublication . Bai, Yun; Xie, Jingjing; Wang, Dongqiang; Zhang, Wanjuan; Li, ChuanManufacturing quality prediction is one of the significant concerns in modern enterprise production management, which provides data support for reliability assessment and parameter optimization, thus improving the intelligent management level of enterprises and helping achieve high-quality products at lower costs. In this paper, an ensemble learning framework using rough knowledge is proposed for manufacturing quality prediction. The proposed model consists of three elements: (1) significant parameters in different production stages are selected based on attribute reduction and decision rule extraction of rough set theory (RS), (2) long short-term memory network (LSTM) is utilized for building the relationship between the significant parameters and manufacturing quality, and (3) the learning performance of the LSTM is reinforced by AdaBoost approach. To estimate the effectiveness of the proposed model, a competition dataset about manufacturing quality control is applied and six models are investigated. The comparison experiments show that the proposed model overwhelms all the comparison models in terms of root-mean-square error, threshold statistics and residuals analysis. In addition, the proposed model has statistically significant difference from all the comparative models. It is recommended from this work that the ensemble learning technique integrating the rough knowledge synchronously improves the sensitivity and regression capacity of the model.
- Regression modeling for enterprise electricity consumption: A comparison of recurrent neural network and its variantsPublication . Bai, Yun; Xie, Jingjing; Liu, Chao; Tao, Ying; Zeng, Bo; Li, ChuanEffective electricity consumption forecasting is extremely significant for enterprises' electricity planning which can provide data support for production decision, thus improving the level of enterprises' clean production. In recent years, recurrent neural network (RNN) and its variants have led to extensive research for time series forecasting. However, the performance and selection of these models in enterprise electricity forecasting have not been reported. With this study, we attempted to back some of these solutions with experimental results. This paper focused on a comparison for daily enterprise electricity consumption forecasting using different RNN models, i.e, standard RNN, long short-term memory-based RNN (LSTM), and gated recurrent unit-based RNN (GRU). To test their regression performance, three Chinese enterprises with different scales of electricity consumption are investigated. The comparison results show that the LSTM and the GRU models are slightly better than that of the RNN in terms of normalized root-mean-square error, mean absolute percentage error and threshold statistic. Moreover, the GRU model with the simplest structure is significantly different from the RNN, but not from LSTM in terms of Friedman testing. Hence the GRU model can be regarded as the first candidate for the enterprise electricity consumption forecasting in the future work.