Superheat Degree Recognition of Aluminum Electrolysis Cell Using Unbalance Double Hierarchy Hesitant Linguistic Petri Nets
Author:
Affiliation:
1. School of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou, China
2. School of Automation, Central South University, Changsha, China
Funder
National Natural Science Foundation of China
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Instrumentation
Link
http://xplorestaging.ieee.org/ielx7/19/10012124/10107420.pdf?arnumber=10107420
Reference43 articles.
1. Knowledge representation and reasoning with industrial application using interval-valued intuitionistic fuzzy Petri nets and extended TOPSIS
2. A mechanism knowledge-driven method for identifying the pseudo dissolution hysteresis coefficient in the industrial aluminium electrolysis process
3. Experiential knowledge representation and reasoning based on linguistic Petri nets with application to aluminum electrolysis cell condition identification
4. A state-of the-art survey of TOPSIS applications
5. An improved TOPSIS-based multi-criteria decision-making approach for evaluating the working condition of the aluminum reduction cell
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