Improved artificial neural network training based on response surface methodology for membrane flux prediction
This paper presents an improved artificial neural network (ANN) training using response surface methodology (RSM) optimization for membrane flux prediction. The improved ANN utilizes the design of experiment (DoE) technique to determine the neural network parameters. The technique has the advantage...
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2022
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my.utm.1032442023-10-24T09:58:40Z http://eprints.utm.my/103244/ Improved artificial neural network training based on response surface methodology for membrane flux prediction Ibrahim, Syahira Abdul Wahab, Norhaliza TK Electrical engineering. Electronics Nuclear engineering This paper presents an improved artificial neural network (ANN) training using response surface methodology (RSM) optimization for membrane flux prediction. The improved ANN utilizes the design of experiment (DoE) technique to determine the neural network parameters. The technique has the advantage of training performance, with a reduced training time and number of repetitions in achieving good model prediction for the permeate flux of palm oil mill effluent. The conventional training process is performed by the trial-and-error method, which is time consuming. In this work, Levenberg–Marquardt (lm) and gradient descent with momentum (gdm) training functions are used, the feed-forward neural network (FFNN) structure is applied to predict the permeate flux, and airflow and transmembrane pressure are the input variables. The network parameters include the number of neurons, the learning rate, the momentum, the epoch, and the training functions. To realize the effectiveness of the DoE strategy, central composite design is incorporated into neural network methodology to achieve both good model accuracy and improved training performance. The simulation results show an improvement of more than 50% of training performance, with less repetition of the training process for the RSM-based FFNN (FFNN-RSM) compared with the conventional-based FFNN (FFNN-lm and FFNN-gdm). In addition, a good accuracy of the models is achieved, with a smaller generalization error. MDPI 2022-08 Article PeerReviewed application/pdf en http://eprints.utm.my/103244/1/NorhalizaAbdulWahab2022_ImprovedArtificialNeuralNetwork.pdf Ibrahim, Syahira and Abdul Wahab, Norhaliza (2022) Improved artificial neural network training based on response surface methodology for membrane flux prediction. Membranes, 12 (8). pp. 1-25. ISSN 2077-0375 http://dx.doi.org/10.3390/membranes12080726 DOI:10.3390/membranes12080726 |
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TK Electrical engineering. Electronics Nuclear engineering Ibrahim, Syahira Abdul Wahab, Norhaliza Improved artificial neural network training based on response surface methodology for membrane flux prediction |
| description |
This paper presents an improved artificial neural network (ANN) training using response surface methodology (RSM) optimization for membrane flux prediction. The improved ANN utilizes the design of experiment (DoE) technique to determine the neural network parameters. The technique has the advantage of training performance, with a reduced training time and number of repetitions in achieving good model prediction for the permeate flux of palm oil mill effluent. The conventional training process is performed by the trial-and-error method, which is time consuming. In this work, Levenberg–Marquardt (lm) and gradient descent with momentum (gdm) training functions are used, the feed-forward neural network (FFNN) structure is applied to predict the permeate flux, and airflow and transmembrane pressure are the input variables. The network parameters include the number of neurons, the learning rate, the momentum, the epoch, and the training functions. To realize the effectiveness of the DoE strategy, central composite design is incorporated into neural network methodology to achieve both good model accuracy and improved training performance. The simulation results show an improvement of more than 50% of training performance, with less repetition of the training process for the RSM-based FFNN (FFNN-RSM) compared with the conventional-based FFNN (FFNN-lm and FFNN-gdm). In addition, a good accuracy of the models is achieved, with a smaller generalization error. |
| format |
Article |
| author |
Ibrahim, Syahira Abdul Wahab, Norhaliza |
| author_facet |
Ibrahim, Syahira Abdul Wahab, Norhaliza |
| author_sort |
Ibrahim, Syahira |
| title |
Improved artificial neural network training based on response surface methodology for membrane flux prediction |
| title_short |
Improved artificial neural network training based on response surface methodology for membrane flux prediction |
| title_full |
Improved artificial neural network training based on response surface methodology for membrane flux prediction |
| title_fullStr |
Improved artificial neural network training based on response surface methodology for membrane flux prediction |
| title_full_unstemmed |
Improved artificial neural network training based on response surface methodology for membrane flux prediction |
| title_sort |
improved artificial neural network training based on response surface methodology for membrane flux prediction |
| publisher |
MDPI |
| publishDate |
2022 |
| url |
http://eprints.utm.my/103244/1/NorhalizaAbdulWahab2022_ImprovedArtificialNeuralNetwork.pdf http://eprints.utm.my/103244/ http://dx.doi.org/10.3390/membranes12080726 |
| _version_ |
1781777667596484608 |
| score |
13.251813 |