Guha, Paramita and Bhatnagar, Taru and Pal, Ishan and Kamboj, Uma and Mishra, Sunita (2017) Prediction of properties of wheat dough using intelligent deep belief networks. Journal of Experimental & Theoretical Artificial Intelligence, 29 (6). pp. 1283-1296. ISSN Print: 0952-813X Online: 1362-3079
Full text not available from this repository.Abstract
In this paper, the rheological and chemical properties of wheat dough are predicted using deep belief networks. Wheat grains are stored at controlled environmental conditions. The internal parameters of grains viz., protein, fat, carbohydrates, moisture, ash are determined using standard chemical analysis and viscosity of the dough is measured using Rheometer. Here, fat, carbohydrates, moisture, ash and temperature are considered as inputs whereas protein and viscosity are chosen as outputs. The prediction algorithm is developed using deep neural network where each layer is trained greedily using restricted Boltzmann machine (RBM) networks. The overall network is finally fine-tuned using standard neural network technique. In most literature, it has been found that fine-tuning is done using back-propagation technique. In this paper, a new algorithm is proposed in which each layer is tuned using RBM and the final network is fine-tuned using deep neural network (DNN). It has been observed that with the proposed algorithm, errors between the actual and predicted outputs are less compared to the conventional algorithm. Hence, the given network can be considered as beneficial as it predicts the outputs more accurately. Numerical results along with discussions are presented.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Wheat Dough, Proximate Analysis, Viscosity, Deep Learning Network, RBM, Deep Belief Networks |
| Subjects: | CSIO > Nano Science and Nano Technology |
| Divisions: | Nano Science and Nano Technology |
| Depositing User: | Ms T Kaur |
| Date Deposited: | 28 Feb 2019 15:47 |
| Last Modified: | 28 Feb 2019 15:47 |
| URI: | http://csioir.csio.res.in/id/eprint/780 |
Actions (login required)
 |
View Item |
