|
1
|
Ebrahimpour M, Yu W, Young B. Artificial neural network modelling for cream cheese fermentation pH prediction at lab and industrial scales. FOOD AND BIOPRODUCTS PROCESSING 2021. [DOI: 10.1016/j.fbp.2020.12.006] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
|
|
2
|
Hernández-Ramos P, Vivar-Quintana A, Revilla I. Estimation of somatic cell count levels of hard cheeses using physicochemical composition and artificial neural networks. J Dairy Sci 2019; 102:1014-1024. [DOI: 10.3168/jds.2018-14787] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2018] [Accepted: 10/31/2018] [Indexed: 11/19/2022]
|
|
3
|
Sicard M, Perrot N, Reuillon R, Mesmoudi S, Alvarez I, Martin S. A viability approach to control food processes: Application to a Camembert cheese ripening process. Food Control 2012. [DOI: 10.1016/j.foodcont.2011.07.007] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
|
|
4
|
Salehi H, Amiri M, Esfandyari M. Using Artificial Neural Network (ANN) for Manipulating Energy Gain of Nansulate Coating. J Nanotechnol Eng Med 2011. [DOI: 10.1115/1.4003500] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
In this work, an extensive experimental data of Nansulate coating from NanoTechInc were applied to develop an artificial neural network (ANN) model. The Levenberg–Marquart algorithm has been used in network training to predict and calculate the energy gain and energy saving of Nansulate coating. By comparing the obtained results from ANN model with experimental data, it was observed that there is more qualitative and quantitative agreement between ANN model values and experimental data results. Furthermore, the developed ANN model shows more accurate prediction over a wide range of operating conditions. Also, maximum relative error of 3% was observed by comparison of experimental and ANN simulation results.
Collapse
Affiliation(s)
- Hadi Salehi
- Department of Chemical Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
| | - Mosayyeb Amiri
- Department of Chemical Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
| | - Morteza Esfandyari
- Department of Chemical Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
| |
Collapse
|
|
5
|
|
|
6
|
|
|
7
|
The optimisation of neural network parameters using Taguchi’s design of experiments approach: an application in manufacturing process modelling. Neural Comput Appl 2005. [DOI: 10.1007/s00521-005-0470-3] [Citation(s) in RCA: 55] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
|
|
8
|
Sukthomya W, Tannock JD. Taguchi experimental design for manufacturing process optimisation using historical data and a neural network process model. INTERNATIONAL JOURNAL OF QUALITY & RELIABILITY MANAGEMENT 2005. [DOI: 10.1108/02656710510598393] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
|