Data-Driven Hybrid Neural Fuzzy Network and ARX Modeling Approach to Practical Industrial Process Identification
Feng Li, Tian Zheng, Naibao He, Qingfeng Cao
Abstract
Dear editor, This letter presents a practical industrial process identification scheme. More specifically, to improve the identification accuracy of practical process, a decoupled identification scheme is developed based on neural fuzzy network and autoregressive exogenous (ARX) model, which is based on multi-signal sources. The multiple signal sources include binary signals and random signals. Experimental results of pH neutralization process show that developed identifi-cation scheme can provide accurate identification accuracy.
Topics & Concepts
Identification (biology)Autoregressive modelProcess (computing)Artificial neural networkScheme (mathematics)Computer scienceIdentification schemeSIGNAL (programming language)Fuzzy logicArtificial intelligenceBinary numberSystem identificationData miningMachine learningPattern recognition (psychology)MathematicsStatisticsProgramming languageOperating systemMeasure (data warehouse)BotanyMathematical analysisArithmeticBiologyFault Detection and Control SystemsAdvanced Algorithms and ApplicationsNeural Networks and Applications