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    An enhanced neural network model for predictive control of granule quality characteristics

    , Article Scientia Iranica ; Volume 18, Issue 3 E , 2011 , Pages 722-730 ; 10263098 (ISSN) Neshat, N ; Mahloojifl, H ; Kazemi, A ; Sharif University of Technology
    An integrated approach is presented for predicting granule particle size using Partial Correlation (PC) analysis and Artificial Neural Networks (ANNs). In this approach, the proposed model is an abstract form from the ANN model, which intends to reduce model complexity via reducing the dimension of the input set and consequently improving the generalization capability of the model. This study involves comparing the capability of the proposed model in predicting granule particle size with those obtained from ANN and Multi Linear Regression models, with respect to some indicators. The numerical results confirm the superiority of the proposed model over the others in the prediction of granule... 

    An intelligent approach for improved predictive control of spray drying process

    , Article INES 2010 - 14th International Conference on Intelligent Engineering Systems, Proceedings, 5 May 2010 through 7 May 2010, Las Palmas of Gran Canaria ; 2010 , Pages 127-136 ; 9781424476527 (ISBN) Azadeh, A ; Neshat, N ; Saberi, M ; Sharif University of Technology
    A flexible meta modelling approach is presented to predictive control of a drying process using Adaptive Neuro-Fuzzy Inference System (ANFIS), Artificial Neural Network (ANN) and Partial Least Squares (PLS) analysis. In the proposed approach, the PLS analysis is used to pre-process actual data and to provide the necessary background to apply ANN and ANFIS approaches. A reasonable section of this study is assigned to the modelling with aim at predicting the granule particle size and executing by ANFIS and ANN. ANN hold the promise of being capable of producing non-linear models, being able to work under noise conditions and being fault tolerant to the loss of neurons or connections. Also, the...