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Modeling pH Neutralization Process using Fuzzy Dynamic Neural Units Approaches
Author(s) -
Lyes Saad Saoud,
F. Rahmoune,
Victor Tourtchine,
Kamel Baddari
Publication year - 2011
Publication title -
international journal of computer applications
Language(s) - English
Resource type - Journals
ISSN - 0975-8887
DOI - 10.5120/3375-4666
Subject(s) - computer science , process (computing) , neutralization , fuzzy logic , artificial neural network , artificial intelligence , process engineering , data mining , operating system , virology , engineering , virus , biology
paper, a new architecture combining dynamic neural units and fuzzy logic approaches is proposed for a complex chemical process modeling. Such processes need a particular care where the designer constructs the neural network, the fuzzy and the fuzzy neural network models which are very useful in black box modeling. The proposed architecture is specified to the pH chemical reactor due to its large existence in the real industrial life and it is a realistic dynamic nonlinear system to demonstrate the feasibility and the performance of the founding results using the fuzzy dynamic neural units. A comparison was made between four strategies, the fuzzy modeling, the recurrent neural networks, the dynamic recurrent neural networks and the fuzzy dynamic neural units. Keywordsprocess; Dynamic neural units; Nonlinear system identification; Fuzzy modeling.

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