Random vector functional-link net based pedestrian detection using multi-feature combination

2013 ◽  
Author(s):  
Zhihui Wang ◽  
Sook Yoon ◽  
Shan Juan Xie ◽  
Yu Lu ◽  
Dong Sun Park
Keyword(s):  
Random Vector ◽  
Pedestrian Detection ◽  
Feature Combination ◽  
Functional Link

scholarly journals A High Accuracy Pedestrian Detection System Combining a Cascade AdaBoost Detector and Random Vector Functional-Link Net

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Zhihui Wang ◽  
Sook Yoon ◽  
Shan Juan Xie ◽  
Yu Lu ◽  
Dong Sun Park
Keyword(s):  
Machine Learning ◽  
Random Vector ◽  
Detection System ◽  
Pedestrian Detection ◽  
High Accuracy ◽  
Machine Learning Algorithms ◽  
Detection Methods ◽  
Functional Link ◽  
Detection Rates ◽  
Standard Scale

In pedestrian detection methods, their high accuracy detection rates are always obtained at the cost of a large amount of false pedestrians. In order to overcome this problem, the authors propose an accurate pedestrian detection system based on two machine learning methods: cascade AdaBoost detector and random vector functional-link net. During the offline training phase, the parameters of a cascade AdaBoost detector and random vector functional-link net are trained by standard dataset. These candidates, extracted by the strategy of a multiscale sliding window, are normalized to be standard scale and verified by the cascade AdaBoost detector and random vector functional-link net on the online phase. Only those candidates with high confidence can pass the validation. The proposed system is more accurate than other single machine learning algorithms with fewer false pedestrians, which has been confirmed in simulation experiment on four datasets.

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.

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