scholarly journals The Yield Prediction of Synthetic Fuel Production from Pyrolysis of Plastic Waste by Levenberg–Marquardt Approach in Feedforward Neural Networks Model

Polymers ◽  
2019 ◽  
Vol 11 (11) ◽  
pp. 1853 ◽  
Author(s):  
Faisal Abnisa ◽  
Shafferina Dayana Anuar Sharuddin ◽  
Mohd Fauzi bin Zanil ◽  
Wan Mohd Ashri Wan Daud ◽  
Teuku Meurah Indra Mahlia
Keyword(s):  
Neural Networks ◽  
Feedforward Neural Networks ◽  
Optimal Number ◽  
Plastic Waste ◽  
Strongly Correlated ◽  
Fuel Production ◽  
Levenberg Marquardt ◽  
Potential Strategy ◽  
Recycled Plastics ◽  
Hidden Neurons

The conversion of plastic waste into fuel by pyrolysis has been recognized as a potential strategy for commercialization. The amount of plastic waste is basically different for each country which normally refers to non-recycled plastics data; consequently, the production target will also be different. This study attempted to build a model to predict fuel production from different non-recycled plastics data. The predictive model was developed via Levenberg-Marquardt approach in feed-forward neural networks model. The optimal number of hidden neurons was selected based on the lowest total of the mean square error. The proposed model was evaluated using the statistical analysis and graphical presentation for its accuracy and reliability. The results showed that the model was capable to predict product yields from pyrolysis of non-recycled plastics with high accuracy and the output values were strongly correlated with the values in literature.

scholarly journals Building Sparse Deep Feedforward Networks using Tree Receptive Fields

2018 ◽  
Author(s):  
Xiaopeng Li ◽  
Zhourong Chen ◽  
Nevin L. Zhang
Keyword(s):  
Neural Networks ◽  
Receptive Field ◽  
Recurrent Neural Networks ◽  
Receptive Fields ◽  
Feedforward Neural Networks ◽  
Classification Performance ◽  
Current Level ◽  
Strongly Correlated ◽  
Feedforward Networks ◽  
Sparse Connectivity

Sparse connectivity is an important factor behind the success of convolutional neural networks and recurrent neural networks. In this paper, we consider the problem of learning sparse connectivity for feedforward neural networks (FNNs). The key idea is that a unit should be connected to a small number of units at the next level below that are strongly correlated. We use Chow-Liu's algorithm to learn a tree-structured probabilistic model for the units at the current level, use the tree to identify subsets of units that are strongly correlated, and introduce a new unit with receptive field over the subsets. The procedure is repeated on the new units to build multiple layers of hidden units. The resulting model is called a TRF-net. Empirical results show that, when compared to dense FNNs, TRF-net achieves better or comparable classification performance with much fewer parameters and sparser structures. They are also more interpretable.

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