Application of feedforward artificial neural networks to improve process control of PID-based control algorithms

Computers and Chemical Engineering

Volumn 24, Issue 2-7, 2000, Pages 853-858

  Martins, Fernando G ^a   Coelho, Manuel A N ^a  

^a UNIVERSITY OF PORTO   (Portugal)

Author keywords

Feedforward artificial neural networks; Process control; Proportional, integral, and derivative based control

Indexed keywords

ALGORITHMS; CASCADE CONTROL SYSTEMS; CHEMICAL REACTORS; COMPUTER SIMULATION; MATHEMATICAL MODELS; PROCESS CONTROL; TEMPERATURE CONTROL; THREE TERM CONTROL SYSTEMS; TWO TERM CONTROL SYSTEMS;

BATCH JACKETED REACTOR; FEEDFORWARD ARTIFICIAL NEURAL NETWORK; PID BASED CONTROL ALGORITHMS;

FEEDFORWARD NEURAL NETWORKS;

CONTROL ALGORITHM; NEURAL NETWORK; PROCESS CONTROL; PROPORTIONAL CONTROL SYSTEM;

ALGORITHM; ARTIFICIAL NEURAL NETWORK; CONFERENCE PAPER; CONTROL SYSTEM; MATHEMATICAL ANALYSIS; PROCESS CONTROL;

EID: 0034661261     PISSN: 00981354     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0098-1354(00)00339-2     Document Type: Conference Paper

References (5)

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3. Seborg, D. E., Edgar, T. F., & Mellichamp, D. A. (1989). Process dynamics and control. New York: Wiley.
4. Tsen, A. Y.-D., Jang, S. S., & Wong, D. S. H. (1996). Predictive control of quality in batch polymerization using hybrid ANN models. American Institute of Chemical Engineering Journal, 42(2), 455-465.
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