Prediction of power consumption and water productivity of seawater greenhouse system using random vector functional link network integrated with artificial ecosystem-based optimization

2020 ◽  
Vol 144 ◽  
pp. 322-329 ◽  
Author(s):  
F.A. Essa ◽  
Mohamed Abd Elaziz ◽  
Ammar H. Elsheikh
Keyword(s):  
Power Consumption ◽  
Random Vector ◽  
Water Productivity ◽  
Functional Link ◽  
Artificial Ecosystem ◽  
Seawater Greenhouse

Utilization of random vector functional link integrated with manta ray foraging optimization for effluent prediction of wastewater treatment plant

2021 ◽  
Vol 298 ◽  
pp. 113520
Author(s):  
Khaled Elmaadawy ◽  
Mohamed Abd Elaziz ◽  
Ammar H. Elsheikh ◽  
Ahmed Moawad ◽  
Bingchuan Liu ◽  
...  
Keyword(s):  
Wastewater Treatment ◽  
Random Vector ◽  
Wastewater Treatment Plant ◽  
Treatment Plant ◽  
Functional Link ◽  
Manta Ray

scholarly journals A Power Transformer Fault Diagnosis Method Based on Random Vector Functional-Link Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Qian Wang ◽  
Shinan Wang ◽  
Rong Shi ◽  
Yong Li
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Network Model ◽  
Random Vector ◽  
Network Structure ◽  
Theoretical Basis ◽  
Parameter Selection ◽  
Functional Link ◽  
Transformer Fault ◽  
Hidden Nodes

The random vector functional link (RVFL) network is suitable for solving nonlinear problems from transformer fault symptoms and different fault types due to its simple structure and strong generalization ability. However, the RVFL network has a disadvantage in that the network structure, and parameters are basically determined by experiences. In this paper, we proposed a method to improve the RVFL neural network algorithm by introducing the concept of hidden node sensitivity, classify each hidden layer node, and remove nodes with low sensitivity. The simplified network structure could avoid interfering nodes and improve the global search capability. The five characteristic gases produced by transformer faults are divided into two groups. A fault diagnosis model of three layers with four classifiers was built. We also investigated the effects of the number of hidden nodes and scale factors on RVFL network learning ability. Simulation results show that the number of implicit layer nodes has a large impact on the network model when the number of input dimensions is small. The network requires a higher number of implicit layer neurons and a smaller threshold range. The size of the scale factor has significant influence on the network model with larger input dimension. This paper describes the theoretical basis for parameter selection in RVFL neural networks. The theoretical basis for the selection of the number of hidden nodes, and the scale factor is derived. The importance of parameter selection for the improvement of diagnostic accuracy is verified through simulation experiments in transformer fault diagnosis.

Sign in / Sign up

Export Citation Format

Share Document