scholarly journals A GMDH-type neural network with multi-filter feature selection for the prediction of transition temperatures of bent-core liquid crystals

RSC Advances ◽  
2016 ◽  
Vol 6 (102) ◽  
pp. 99676-99684 ◽  
Author(s):  
Davor Antanasijević ◽  
Jelena Antanasijević ◽  
Viktor Pocajt ◽  
Gordana Ušćumlić
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Feature Selection ◽  
Transition Temperatures ◽  
Chi Square ◽  
Qspr Study ◽  
Selection For ◽  
Filter Approach ◽  
Selection Of Descriptors ◽  
Selection Of

The QSPR study on transition temperatures of five-ring bent-core LCs was performed using GMDH-type neural networks. A novel multi-filter approach, which combines chi square ranking, v-WSH and GMDH algorithm was used for the selection of descriptors.

Prediction of slip in cable-drum systems using structured neural networks

2013 ◽  
Vol 228 (3) ◽  
pp. 441-456 ◽  
Author(s):  
Ergin Kilic ◽  
Melik Dolen
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Size ◽  
Network Architectures ◽  
Network Development ◽  
Low Velocity ◽  
Error Band ◽  
Artificial Neural ◽  
Recurrent Type ◽  
Selection Of

This study focuses on the slip prediction in a cable-drum system using artificial neural networks for the prospect of developing linear motion sensing scheme for such mechanisms. Both feed-forward and recurrent-type artificial neural network architectures are considered to capture the slip dynamics of cable-drum mechanisms. In the article, the network development is presented in a progressive (step-by-step) fashion for the purpose of not only making the design process transparent to the readers but also highlighting the corresponding challenges associated with the design phase (i.e. selection of architecture, network size, training process parameters, etc.). Prediction performances of the devised networks are evaluated rigorously via an experimental study. Finally, a structured neural network, which embodies the network with the best prediction performance, is further developed to overcome the drift observed at low velocity. The study illustrates that the resulting structured neural network could predict the slip in the mechanism within an error band of 100 µm when an absolute reference is utilized.

Redundancy-Controlled Feature Selection for Fuzzy Neural Networks

2021 ◽  
Author(s):  
Tao Gao ◽  
Xiao Bai ◽  
Liang Zhang ◽  
Chen Wang ◽  
Jian Wang
Keyword(s):  
Neural Networks ◽  
Feature Selection ◽  
Fuzzy Neural Networks ◽  
Fuzzy Neural ◽  
Selection For
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