scholarly journals Neural network for regression problems with reduced training sets

2017 ◽  
Vol 95 ◽  
pp. 1-9 ◽  
Author(s):  
Mohammad Bataineh ◽  
Timothy Marler
Keyword(s):  
Neural Network ◽  
Regression Problems ◽  
Training Sets

Molecular Generation Targeting Desired Electronic Properties via Deep Generative Models

2019 ◽  
Author(s):  
Qi Yuan ◽  
Alejandro Santana-Bonilla ◽  
Martijn Zwijnenburg ◽  
Kim Jelfs
Keyword(s):  
Neural Network ◽  
Electronic Properties ◽  
Transfer Learning ◽  
Recurrent Neural Network ◽  
Chemical Space ◽  
Generative Models ◽  
Molecular Features ◽  
Donor Acceptor ◽  
Homo Lumo ◽  
Training Sets

<p>The chemical space for novel electronic donor-acceptor oligomers with targeted properties was explored using deep generative models and transfer learning. A General Recurrent Neural Network model was trained from the ChEMBL database to generate chemically valid SMILES strings. The parameters of the General Recurrent Neural Network were fine-tuned via transfer learning using the electronic donor-acceptor database from the Computational Material Repository to generate novel donor-acceptor oligomers. Six different transfer learning models were developed with different subsets of the donor-acceptor database as training sets. We concluded that electronic properties such as HOMO-LUMO gaps and dipole moments of the training sets can be learned using the SMILES representation with deep generative models, and that the chemical space of the training sets can be efficiently explored. This approach identified approximately 1700 new molecules that have promising electronic properties (HOMO-LUMO gap <2 eV and dipole moment <2 Debye), 6-times more than in the original database. Amongst the molecular transformations, the deep generative model has learned how to produce novel molecules by trading off between selected atomic substitutions (such as halogenation or methylation) and molecular features such as the spatial extension of the oligomer. The method can be extended as a plausible source of new chemical combinations to effectively explore the chemical space for targeted properties.</p>

scholarly journals An improved algorithm for neural network classification of imbalanced training sets

1993 ◽  
Vol 4 (6) ◽  
pp. 962-969 ◽  
Author(s):  
R. Anand ◽  
K.G. Mehrotra ◽  
C.K. Mohan ◽  
S. Ranka
Keyword(s):  
Neural Network ◽  
Neural Network Classification ◽  
Training Sets ◽  
Improved Algorithm

Implicit Neural Network for Implicit Data Regression Problems

2021 ◽  
pp. 187-195
Author(s):  
Zhibin Miao ◽  
Jinghui Zhong ◽  
Peng Yang ◽  
Shibin Wang ◽  
Dong Liu
Keyword(s):  
Neural Network ◽  
Data Regression ◽  
Regression Problems ◽  
Implicit Data

scholarly journals PREDETERMINED MOVEMENT OF MOBILE ROBOT USING NEURAL NETWORKS

2014 ◽  
pp. 64-68
Author(s):  
Oleh Adamiv ◽  
Vasyl Koval ◽  
Iryna Turchenko
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Mobile Robot ◽  
Formation Process ◽  
Robot Control ◽  
Experimental Results ◽  
The Road ◽  
Mobile Robot Control ◽  
Different Parts ◽  
Training Sets

This paper describes the experimental results of neural networks application for mobile robot control on predetermined trajectory of the road. There is considered the formation process of training sets for neural network, their structure and simulating features. Researches have showed robust mobile robot movement on different parts of the road.

Evaluation of Breakdown Voltage and Water Content in Transformer Oil Using Multi Frequency Ultrasonic and Generalized Regression Neural Network

2021 ◽  
Vol 16 (3) ◽  
pp. 387-394
Author(s):  
Yang Su ◽  
Ming-Hui Liu ◽  
Xu-Hui Kong ◽  
Chen-Jun Guo ◽  
Jiang Zhu ◽  
...  
Keyword(s):  
Neural Network ◽  
Water Content ◽  
Breakdown Voltage ◽  
Power Transformer ◽  
Oil Quality ◽  
Transformer Oil ◽  
Generalized Regression Neural Network ◽  
Test Sets ◽  
Generalized Regression ◽  
Training Sets

Power transformer is regarded as one of the crucial part of electrical power transmission and distribution system. The quality of transformer oil can directly affect the operation of the power transformer, and breakdown voltage (BDV) and water content are the two main parameters of transformer oil quality. Monitoring the BDV and water content of transformer oil is considered as an important method to evaluate the safe operation of power systems. This work proposes the measurement of BDV and water content in transformer oil using multi frequency ultrasonic and generalized regression neural network (GRNN). The BDV and water content of all 210 samples were firstly tested according to the traditional testing methods and the multi frequency ultra-sonic technology, separately. And then the 210 samples were randomly divided into training sets and test sets. The obtained multi frequency ultrasonic data were set as the input of GRNN, and the BDV and water content as the output of GRNN. Moreover, the 20-fold-cross-validation was incorporated to obtain the best smoothing factor δ for GRNN. Finally, the GRNN model was trained by the training sets with δ =4.54 and was evaluated with the test sets. All results show that the lower BDV or the higher water content of the sample will cause greater ultrasonic sound attenuation, and the prediction accuracy of the prediction model for BDV and water con-tent in oil is up to 95%. It provides a new method for evaluating the health of transformer oil.

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