Illumination estimation via random vector functional link based on improved arithmetic optimization algorithm

2021 ◽  
Author(s):  
Xiangqi Liu ◽  
Zhiyu Zhou
Keyword(s):  
Random Vector ◽  
Optimization Algorithm ◽  
Functional Link ◽  
Illumination Estimation

Classification of clothing images based on a parallel convolutional neural network and random vector functional link optimized by the grasshopper optimization algorithm

2021 ◽  
pp. 004051752110592
Author(s):  
Zhiyu Zhou ◽  
Wenxiong Deng ◽  
Yaming Wang ◽  
Zefei Zhu
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Convolutional Neural Network ◽  
Random Vector ◽  
Optimization Algorithm ◽  
Activation Function ◽  
Support Vector ◽  
Functional Link ◽  
Grasshopper Optimization Algorithm ◽  
Grasshopper Optimization

To improve accuracy in clothing image recognition, this paper proposes a clothing classification method based on a parallel convolutional neural network (PCNN) combined with an optimized random vector functional link (RVFL). The method uses the PCNN model to extract features of clothing images. Then, the structure-intensive and dual-channel convolutional neural network (i.e., the PCNN) is used to solve the problems of traditional convolutional neural networks (e.g., limited data and prone to overfitting). Each convolutional layer is followed by a batch normalization layer, and the leaky rectified linear unit activation function and max-pooling layers are used to improve the performance of the feature extraction. Then, dropout layers and fully connected layers are used to reduce the amount of calculation. The last layer uses the RVFL as optimized by the grasshopper optimization algorithm to replace the SoftMax layer and classify the features, further improving the stability and accuracy of classification. In this study, two aspects of the classification (feature extraction and feature classification) are improved, effectively improving the accuracy. The experimental results show that on the Fashion-Mnist dataset, the accuracy of the algorithm in this study reaches 92.93%. This value is 1.36%, 2.05%, 0.65%, and 3.76% higher than that of the local binary pattern (LBP)-support vector machine (SVM), histogram of oriented gradients (HOG)-SVM, LBP-HOG-SVM, and AlexNet-sparse representation-based classifier algorithms, respectively, effectively demonstrating the classification performance of the algorithm.

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.

Multi-kernel based Random Vector Functional Link Neural Network for Short-term Prediction of Wind Speed

2021 ◽  
Author(s):  
Sniedha Sarangi ◽  
Prdipta Kishore Dash ◽  
Badri Narayan Sahoo ◽  
Ranjeeta Bisoi
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Random Vector ◽  
Short Term ◽  
Functional Link ◽  
Term Prediction ◽  
Short Term Prediction ◽  
Functional Link Neural Network

scholarly journals Stacked autoencoder based deep random vector functional link neural network for classification

2019 ◽  
Vol 85 ◽  
pp. 105854 ◽  
Author(s):  
Rakesh Katuwal ◽  
P.N. Suganthan
Keyword(s):  
Neural Network ◽  
Random Vector ◽  
Functional Link ◽  
Stacked Autoencoder ◽  
Functional Link Neural Network

Discriminative manifold random vector functional link neural network for rolling bearing fault diagnosis

2021 ◽  
Vol 211 ◽  
pp. 106507
Author(s):  
Xin Li ◽  
Yu Yang ◽  
Niaoqing Hu ◽  
Zhe Cheng ◽  
Junsheng Cheng
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Random Vector ◽  
Rolling Bearing ◽  
Functional Link ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Functional Link Neural Network

Application of fast curvelet Tsallis entropy and kernel random vector functional link network for automated detection of multiclass brain abnormalities

2019 ◽  
Vol 77 ◽  
pp. 101656 ◽  
Author(s):  
Deepak Ranjan Nayak ◽  
Ratnakar Dash ◽  
Banshidhar Majhi ◽  
U. Rajendra Acharya
Keyword(s):  
Random Vector ◽  
Automated Detection ◽  
Tsallis Entropy ◽  
Brain Abnormalities ◽  
Functional Link
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