The development of a decision model for liquidity analysis

An Intelligent Switching Control for the Intervals of Concentration and Flow-rate of Underflow Slurry in a Mixed Separation Thickener

“…The "If <premise> then <conclusion>" rule-based reasoning (Li, Shue, & Shiue. 2000;Lin, Tseng, & Teng.…”

Conditions identification model based on LLNFM and RBR in cement raw meal calcination process

“…Consequently, the calciner temperature cannot be guaranteed to stay within its targeted range in normal condition, and 'C5 feeding tube clogged' fault could happen in abnormal condition. Motivated by the analysis above, a condition identification model has been proposed by combing local linear neuro-fuzzy model (LLNFM) [5] with rule-based reasoning (RBR) [6] when the boundary conditions change. The LLNFM was applied to the model to predict the output temperature of the preheater C5 using input variables.…”