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Iranian Journal of Chemistry and Chemical Engineering، جلد ۴۳، شماره ۱۰، صفحات ۳۸۵۴-۳۸۶۲
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عنوان انگلیسی Optimization of Betulinic Acid Ester Enzymatic Synthesis by Artificial Intelligence
چکیده انگلیسی مقاله The reaction conditions of the enzymatic synthesis of betulinic acid ester are a practical and vital reaction. Its results have been previously modeled by artificial intelligence. In this studyr, mentioned reaction has been not only simulated but also optimized by multi-objective meta-heuristic algorithms. The Multi-Layer Perceptron (MLP) is a type of feed forward artificial neural network (ANN) which its performance improves by reduction of train and test data errors. It depends on the trained method and the number of neurons in the hidden layer. In an appropriate ANN, errors for train data and test data have to be closed. Radial Basis Function (RBF) hasn't been utilized as ANN already. The RBF consists of an extraordinary advantage: it determines the number of neurons due to desired error and its parameters have been set by advanced particle swarm optimization (PSO) algorithm. Further, PSO's parameters including c1, c2, and ω are determined as fuzzy. Finally, the results of the proposed method will be compared with those of previous methods.
کلیدواژه‌های انگلیسی مقاله train data,test data,error minimization,goal,spread
نویسندگان مقاله Ali Reza Hosseinpour |
Department of Electrical Engineering, Faculty of Engineering, University of Zabol, Zabol, I.R. IRAN

Mahdiye Poorsargol |
Department of Chemistry, Faculty of Science, University of Zabol, Zabol, I.R. IRAN

نشانی اینترنتی https://ijcce.ac.ir/article_716856_b4a099509d26b892bb6c41e731436f61.pdf
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