Search Results - unsupervised sparse‐autoencoder‐based deep neural network

Robust approach for AMC in frequency selective fading scenarios using unsupervised sparse-autoencoder-based deep neural network by 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…”
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Denoising Sparse Autoencoder-Based Ictal EEG Classification by 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…”
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Sparse Autoencoder-based Multi-head Deep Neural Networks for Machinery Fault Diagnostics with Detection of Novelties by 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…”
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Sparse Autoencoder-based Multi-head Deep Neural Networks for Machinery Fault Diagnostics with Detection of Novelties by 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…”
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Improved sparse autoencoder based artificial neural network approach for prediction of heart disease by 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…”
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Sparse Autoencoder Based Deep Neural Network for Voxelwise Detection of Cerebral Microbleed by 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…”
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Intelligent condition monitoring method for bearing faults from highly compressed measurements using sparse over-complete features by 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…”
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SSAE‐MLP: Stacked sparse autoencoders‐based multi‐layer perceptron for main bearing temperature prediction of large‐scale wind turbines by 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…”
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Research on Target Object Recognition Based on Transfer-Learning Convolutional SAE in Intelligent Urban Construction by 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…”
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Compressive Sampling and Deep Neural Network (CS‐DNN) by Ahmed, Hosameldin, Nandi, Asoke K

“…The compressive sampling and sparse autoencoder‐based deep neural network (CS‐SAE‐DNN…”
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Intelligent Bearing Fault Diagnosis Method Combining Compressed Data Acquisition and Deep Learning by 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…”
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Rock mass type prediction for tunnel boring machine using a novel semi-supervised method by 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…”
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Intelligent methods for condition monitoring of rolling bearings using vibration data by 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…”
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A deep learning and softmax regression fault diagnosis method for multi-level converter by 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…”
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Effects of deep neural network parameters on classification of bearing faults by 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…”
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