Suchergebnisse - unsupervised sparse‐autoencoder‐based deep neural network

Robust approach for AMC in frequency selective fading scenarios using unsupervised sparse-autoencoder-based deep neural network von Shah, Maqsood Hussain, Dang, Xiaoyu

“… Application of deep learning in the area of automatic modulation classification (AMC) is still evolving. An unsupervised sparse-autoencoder-based deep neural network …”
Volltext

Denoising Sparse Autoencoder-Based Ictal EEG Classification von Qiu, Yang, Zhou, Weidong, Yu, Nana, Du, Peidong

“… The denoising sparse autoencoder (DSAE) is an improved unsupervised deep neural network over sparse autoencoder and denoising autoencoder, which can learn the closest representation of the data …”
Volltext

Sparse Autoencoder-based Multi-head Deep Neural Networks for Machinery Fault Diagnostics with Detection of Novelties von Yang, Zhe, Gjorgjevikj, Dejan, Long, Jianyu, Zi, Yanyang, Zhang, Shaohui, Li, Chuan

“… To this end, a sparse autoencoder-based multi-head Deep Neural Network (DNN) is presented to jointly learn a shared encoding representation for both unsupervised reconstruction and supervised classification of the monitoring data …”
Volltext

Sparse Autoencoder-based Multi-head Deep Neural Networks for Machinery Fault Diagnostics with Detection of Novelties von Zhe Yang, Dejan Gjorgjevikj, Jianyu Long, Yanyang Zi, Shaohui Zhang, Chuan Li

“… of novelties.To this end,a sparse autoencoder-based multi-head Deep Neural Network(DNN)is presented to jointly learn a shared encoding representation for both unsupervised reconstruction and supervised classification of the monitoring …”
Volltext

Improved sparse autoencoder based artificial neural network approach for prediction of heart disease von Mienye, Ibomoiye Domor, Sun, Yanxia, Wang, Zenghui

“… The first stage involves training an improved sparse autoencoder (SAE), an unsupervised neural network, to learn the best representation of the training data …”
Volltext

Sparse Autoencoder Based Deep Neural Network for Voxelwise Detection of Cerebral Microbleed von Yu-Dong Zhang, Xiao-Xia Hou, Yi-Ding Lv, Hong Chen, Yin Zhang, Shui-Hua Wang

“… The sparse autoencoder (SAE) was used to unsupervised feature learning. Then, a deep neural network was established using the learned features …”
Volltext

Intelligent condition monitoring method for bearing faults from highly compressed measurements using sparse over-complete features von Ahmed, H.O.A., Wong, M.L.D., Nandi, A.K.

“… •Uses compressive sensing and sparse over-complete feature learning.•Uses the unsupervised sparse autoencoder for learning feature representations …”
Volltext

SSAE‐MLP: Stacked sparse autoencoders‐based multi‐layer perceptron for main bearing temperature prediction of large‐scale wind turbines von Xiao, Xiaocong, Liu, Jianxun, Liu, Deshun, Tang, Yufei, Dai, Juchuan, Zhang, Fan

“… Then, the multiple sparse autoencoders are stacked to learn the deep features inside the input data by applying the greedy layerwise unsupervised learning algorithm …”
Volltext

Research on Target Object Recognition Based on Transfer-Learning Convolutional SAE in Intelligent Urban Construction von Xie, Bing, Duan, Zhemin, Zheng, Bin, Liu, Liping

“… In this paper, we attempt to apply the deep neural network composed of sparse autoencoders based unsupervised …”
Volltext

Compressive Sampling and Deep Neural Network (CS‐DNN) von Ahmed, Hosameldin, Nandi, Asoke K

“… The compressive sampling and sparse autoencoder‐based deep neural network (CS‐SAE‐DNN …”
Volltext

Intelligent Bearing Fault Diagnosis Method Combining Compressed Data Acquisition and Deep Learning von Sun, Jiedi, Yan, Changhong, Wen, Jiangtao

“… Inspired by the idea of compressed sensing and deep learning, a novel intelligent diagnosis method is proposed for fault identification of rotating machines …”
Volltext

Rock mass type prediction for tunnel boring machine using a novel semi-supervised method von Yu, Honggan, Tao, Jianfeng, Qin, Chengjin, Xiao, Dengyu, Sun, Hao, Liu, Chengliang

“… •A novel semi-supervised framework is proposed to predict geological type ahead of tunnel face.•The semi-supervised framework consists of a feature extractor …”
Volltext

Intelligent methods for condition monitoring of rolling bearings using vibration data von Ahmed, Hosameldin

“… Owing to the importance of rolling bearings in rotating machines, there has been great interest in the development of computational methods for rolling …”
Volltext

A deep learning and softmax regression fault diagnosis method for multi-level converter von Bin Xin, Tianzhen Wang, Tianhao Tang

“… With the single-tube and double-tube fault of seven-level converter, this paper presents a new way to learn the faults feature based on the deep neural network of sparse autoencoder …”
Volltext

Effects of deep neural network parameters on classification of bearing faults von Ahmed, H. O. A., Dennis Wong, M. L., Nandi, A. K.

“… In this paper, we classify roller element bearings fault classes under two and three hidden layers' deep neural network framework based on sparse Autoencoder …”
Volltext