On-line Evolving Cloud-based Model Identification for Production Control

Abstract

In this paper we present an on-line evolving fuzzy cloud-based identification method. The evolving part of the algorithm is improved with new mechanisms. In the part of adding clouds (fuzzy rules) a new condition is implemented in addition to existing ones. Moreover, completely new mechanism for removing the “less active” and “less informative” clouds is introduced. All these mechanisms prevent adding new clouds based on outliers or at least help deleting existing ones with little information. The cloud-based method uses vectorized non-parametric antecedent (IF) part which relies on the local density of the current data with the existing clouds. The parameters of the consequent (THEN) part (functional in this case) were identified using recursive Weight Least Square method.The comparison between the original and the improved algorithm was provided on simulated data input/output signals acquired from Tennessee Eastman (TE) benchmark process. Firstly, most representative production Performance Indicators (pPIs) were chosen, then for each pPI a model was identified. The provided results (quality measures) of the proposed method were evaluated using on-line and off-line 4-step prediction. These were further compared with the results obtained using eFuMo identification tool.