Fuzzy association rule-based set-point adaptive optimization and control for the flotation process

2020 ◽  
Vol 32 (17) ◽  
pp. 14019-14029 ◽  
Author(s):  
Mingxi Ai ◽  
Yongfang Xie ◽  
Shiwen Xie ◽  
Jin Zhang ◽  
Weihua Gui
Keyword(s):  
Association Rule ◽  
Adaptive Optimization ◽  
Flotation Process ◽  
Rule Based ◽  
Set Point ◽  
Optimization And Control ◽  
Fuzzy Association Rule ◽  
And Control

scholarly journals Balancing Energy Efficiency with Indoor Comfort Using Smart Control Agents: A Simulative Case Study

Energies ◽  
2020 ◽  
Vol 13 (23) ◽  
pp. 6228
Author(s):  
Iakovos T. Michailidis ◽  
Roozbeh Sangi ◽  
Panagiotis Michailidis ◽  
Thomas Schild ◽  
Johannes Fuetterer ◽  
...  
Keyword(s):  
Large Scale ◽  
Real Life ◽  
Energy Performance ◽  
Centralized Control ◽  
Optimization Strategy ◽  
Adaptive Optimization ◽  
Reference Case ◽  
Decentralized Optimization ◽  
Optimization And Control ◽  
And Control

Modern literature exhibits numerous centralized control approaches—event-based or model assisted—for tackling poor energy performance in buildings. Unfortunately, even novel building optimization and control (BOC) strategies commonly suffer from complexity and scalability issues as well as uncertain behavior as concerns large-scale building ecosystems—a fact that hinders their practical compatibility and broader applicability. Moreover, decentralized optimization and control approaches trying to resolve scalability and complexity issues have also been proposed in literature. Those approaches usually suffer from modeling issues, utilizing an analytically available formula for the overall performance index. Motivated by the complications in existing strategies for BOC applications, a novel, decentralized, optimization and control approach—referred to as Local for Global Parameterized Cognitive Adaptive Optimization (L4GPCAO)—has been extensively evaluated in a simulative environment, contrary to previous constrained real-life studies. The current study utilizes an elaborate simulative environment for evaluating the efficiency of L4GPCAO; extensive simulation tests exposed the efficiency of L4GPCAO compared to the already evaluated centralized optimization strategy (PCAO) and the commercial control strategy that is adopted in the BOC practice (common reference case). L4GPCAO achieved a quite similar performance in comparison to PCAO (with 25% less control parameters at a local scale), while both PCAO and L4GPCAO significantly outperformed the reference BOC practice.

FUSING FUZZY ASSOCIATION RULE-BASED CLASSIFIERS USING SUGENO INTEGRAL WITH ORDERED WEIGHTED AVERAGING OPERATORS

2007 ◽  
Vol 15 (06) ◽  
pp. 717-735 ◽  
Author(s):  
YI-CHUNG HU ◽  
JUNG-FA TSAI
Keyword(s):  
Association Rule ◽  
Fuzzy Classification ◽  
Weighted Averaging ◽  
Averaging Operators ◽  
Rule Based ◽  
Sugeno Integral ◽  
Ordered Weighted Averaging ◽  
Multiple Classifiers ◽  
Fuzzy Association Rule ◽  
Ordered Weighted Averaging Operators

The time or space complexity may considerably increase for a single classifier if all features are taken into account. Thus, it is reasonable to train a single classifier by partial features. Then, a set of multiple classifiers can be generated, and an aggregation of outputs from different classifiers is subsequently performed. The aim of this paper is to propose a classification system with a heuristic fusion scheme in which multiple fuzzy association rule-based classifiers with partial features are combined, and show the feasibility and effectiveness of fusing multiple classifiers through the Sugeno integral extended by ordered weighted averaging operators. In comparison with the Sugeno integral by computer simulations on the iris data and the appendicitis data show that the overall classification accuracy rate could be improved by the Sugeno integral with ordered weighted averaging operators. The experimental results further demonstrate that the proposed method performs well in comparison with other fuzzy or non-fuzzy classification methods.

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