Hierarchical-fuzzy allocation and multi-parameter adjustment prediction for industrial loading optimisation. Issue 1 (31st December 2022)

• Record Type: Journal Article
• Authors: Chen, Zihua; Wang, Chuanli; Jin, Huawei; Li, Jingzhao; Zhang, Shunxiang; Ouyang, Qichun
• Is Part Of: Connection science. Volume 34:Issue 1(2022)
• Page Start: 687
• Page End: 708
• Publication Date: 2022-12-31
• Subjects: Parameters prediction -- industrial loading -- LSTM neural network -- hierarchical fuzzy calculation; Neural computers -- Periodicals; Artificial intelligence -- Periodicals; Cognitive science -- Periodicals; Connectionism -- Periodicals; 006.3
• DOI: 10.1080/09540091.2022.2031887
• Languages: English
• ISSNs: 0954-0091

Abstract

Abstract : Conventional manual-programmable logic controller systems have confronted the problems of the unbalance load and the unreasonable bins allocation in industrial loading field. Furthermore, various optimisation models with multi-agent systems have been proposed for the single-layer scheduling and communicating, which results in either a high time cost or a difficult multi-target regression. In this paper, we propose a hierarchical-fuzzy bins allocation method and a multi-parameter adjustment values prediction model in the multi-agent collaborative control system. The method intuitively achieves topgallant and hierarchical bins allocation by different fuzzy rule bases. The multi-parameter adjustment values prediction model utilising parallel-multi LSTM(PM-LSTM) is located on the accurate multi-parameter prediction. First, new loading reference standards and an abnormal data procession method are adopted for the dataset collection. Second, the LSTM-1 is used to extract the time-series features in the loading process. Third, a two-dimensional and reconstructed matrix integrates comprehensive features with the feature crossover method. The matrix will be used as inputs to predict the adjustment value of multi parameters by the LSTM-2. Finally, the relationship model among multi parameter values is built and fitted. Experiment results show better effects for the reasonable bins allocation and balanced industrial loading.