scholarly journals Deep Belief Network for Feature Extraction of Urban Artificial Targets

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Xiaoai Dai ◽  
Junying Cheng ◽  
Yu Gao ◽  
Shouheng Guo ◽  
Xingping Yang ◽  
...  
Keyword(s):  
Hyperspectral Image ◽  
Image Data ◽  
Original Data ◽  
Deep Belief Network ◽  
Fine Tuning ◽  
Model Parameters ◽  
Layer By Layer ◽  
Restricted Boltzmann Machines ◽  
Belief Network ◽  
Network Algorithm

Reducing the dimension of the hyperspectral image data can directly reduce the redundancy of the data, thus improving the accuracy of hyperspectral image classification. In this paper, the deep belief network algorithm in the theory of deep learning is introduced to extract the in-depth features of the imaging spectral image data. Firstly, the original data is mapped to feature space by unsupervised learning methods through the Restricted Boltzmann Machine (RBM). Then, a deep belief network will be formed by superimposed multiple Restricted Boltzmann Machines and training the model parameters by using the greedy algorithm layer by layer. At the same time, as the objective of data dimensionality reduction is achieved, the underground feature construction of the original data will be formed. The final step is to connect the depth features of the output to the Softmax regression classifier to complete the fine-tuning (FT) of the model and the final classification. Experiments using imaging spectral data showing the in-depth features extracted by the profound belief network algorithm have better robustness and separability. It can significantly improve the classification accuracy and has a good application prospect in hyperspectral image information extraction.

scholarly journals Deep Belief Network Based 3D Models Classification in Building Information Modeling

2015 ◽  
Vol 11 (5) ◽  
pp. 57
Author(s):  
Li Wang ◽  
Zhi-kai Zhao ◽  
Na Xu
Keyword(s):  
3D Model ◽  
Building Information Modeling ◽  
3D Models ◽  
Deep Belief Network ◽  
Information Modeling ◽  
Layer By Layer ◽  
Restricted Boltzmann Machines ◽  
Belief Network ◽  
Model Library ◽  
Building Information

3D models classification is a critical process of Building Information Modeling (BIM). A Deep Learning Approach is proposed to classify 3D models in BIM environment. The ray based feature extraction algorithm is used to extract features of 3D models and form features matrix. The Deep Belief Network constructed by Restricted Boltzmann Machines applies the features matrix and classifies the models adopting the effective training process. The process of training DBN is layer by layer. Experiments were taken on the public 3D model library of PSB model database. The results show that compared with several commonly used classification method, the proposed method of this paper has achieved good results in the 3D model classification for efficiently BIM.

scholarly journals Research on DBN-based Evaluation of Distribution Network Reliability

2021 ◽  
Vol 242 ◽  
pp. 03004
Author(s):  
Kaiyu Zhang ◽  
Shanshan Shi ◽  
Shu Liu ◽  
Junjie Wan ◽  
Lijia Ren
Keyword(s):  
Reliability Analysis ◽  
Network Reliability ◽  
Distribution Network ◽  
Simulation Method ◽  
Deep Belief Network ◽  
Fine Tuning ◽  
Layer By Layer ◽  
Boltzmann Machine ◽  
Training Process ◽  
Belief Network

In order to accurately and efficiently analyze the reliability of distribution network, this paper proposes a method of analyzing the reliability of distribution network based on a deep belief network. The Deep Belief Network (DBN) is composed of limiting Boltzmann machine layer-by-layer stacking. It has a strong advantage of automatic feature extraction, which overcomes the shortcomings of traditional neural networks in extracting data features. The entire training process of DBN can be roughly divided into two stages: pre-training and fine-tuning.First of all, the pre-training of the DBN model is realized by training the Restricted Boltzmann Machine (RBM) layer by layer, then the BP algorithm is used for reverse fine-tuning to complete the training process of the entire network. finally, the reliability analysis of distribution network is performed by the trained DBN. Compared with the BP neural network method and the traditional Monte Carlo simulation method, it is verified that the proposed model of distribution network reliability analysis has high accuracy.

scholarly journals An Improved Deep Belief Network Prediction Model Based on Knowledge Transfer

2020 ◽  
Vol 12 (11) ◽  
pp. 188
Author(s):  
Yue Zhang ◽  
Fangai Liu
Keyword(s):  
Knowledge Transfer ◽  
Prediction Performance ◽  
Deep Belief Network ◽  
Fine Tuning ◽  
Layer By Layer ◽  
Least Squares Regression ◽  
Back Propagation Algorithm ◽  
Belief Network ◽  
Model Based ◽  
Public Data

A deep belief network (DBN) is a powerful generative model based on unlabeled data. However, it is difficult to quickly determine the best network structure and gradient dispersion in traditional DBN. This paper proposes an improved deep belief network (IDBN): first, the basic DBN structure is pre-trained and the learned weight parameters are fixed; secondly, the learned weight parameters are transferred to the new neuron and hidden layer through the method of knowledge transfer, thereby constructing the optimal network width and depth of DBN; finally, the top-down layer-by-layer partial least squares regression method is used to fine-tune the weight parameters obtained by the pre-training, which avoids the traditional fine-tuning problem based on the back-propagation algorithm. In order to verify the prediction performance of the model, this paper conducts benchmark experiments on the Movielens-20M (ML-20M) and Last.fm-1k (LFM-1k) public data sets. Compared with other traditional algorithms, IDBN is better than other fixed models in terms of prediction performance and training time. The proposed IDBN model has higher prediction accuracy and convergence speed.

scholarly journals Deep Belief Network for Spectral–Spatial Classification of Hyperspectral Remote Sensor Data

Sensors ◽  
2019 ◽  
Vol 19 (1) ◽  
pp. 204 ◽  
Author(s):  
Chenming Li ◽  
Yongchang Wang ◽  
Xiaoke Zhang ◽  
Hongmin Gao ◽  
Yao Yang ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Optical Sensors ◽  
Spatial Information ◽  
Hyperspectral Image ◽  
Deep Belief Network ◽  
Fine Tuning ◽  
Sensor Data ◽  
Belief Network

With the development of high-resolution optical sensors, the classification of ground objects combined with multivariate optical sensors is a hot topic at present. Deep learning methods, such as convolutional neural networks, are applied to feature extraction and classification. In this work, a novel deep belief network (DBN) hyperspectral image classification method based on multivariate optical sensors and stacked by restricted Boltzmann machines is proposed. We introduced the DBN framework to classify spatial hyperspectral sensor data on the basis of DBN. Then, the improved method (combination of spectral and spatial information) was verified. After unsupervised pretraining and supervised fine-tuning, the DBN model could successfully learn features. Additionally, we added a logistic regression layer that could classify the hyperspectral images. Moreover, the proposed training method, which fuses spectral and spatial information, was tested over the Indian Pines and Pavia University datasets. The advantages of this method over traditional methods are as follows: (1) the network has deep structure and the ability of feature extraction is stronger than traditional classifiers; (2) experimental results indicate that our method outperforms traditional classification and other deep learning approaches.

scholarly journals Deep Belief Network for Predicting the Predisposition to Lung Cancer in TP53 Gene

2020 ◽  
pp. 171-177 ◽  
Author(s):  
Zahraa Naser Shahweli
Keyword(s):  
Neural Network ◽  
Lung Cancer ◽  
Smoking Habit ◽  
Back Propagation ◽  
Tp53 Gene ◽  
Back Propagation Neural Network ◽  
Biological Data ◽  
Deep Belief Network ◽  
Restricted Boltzmann Machines ◽  
Belief Network

Lung cancer, similar to other cancer types, results from genetic changes. However, it is considered as more threatening due to the spread of the smoking habit, a major risk factor of the disease. Scientists have been collecting and analyzing the biological data for a long time, in attempts to find methods to predict cancer before it occurs. Analysis of these data requires the use of artificial intelligence algorithms and neural network approaches. In this paper, one of the deep neural networks was used, that is the enhancer Deep Belief Network (DBN), which is constructed from two Restricted Boltzmann Machines (RBM). The visible nodes for the first RBM are 13 nodes and 8 nodes in each hidden layer for the two RBMs. The enhancer DBN was trained by Back Propagation Neural Network (BPNN), where the data sets were divided into 6 folds, each is split into three partitions representing the training, validation, and testing. It is worthy to note that the proposed enhancer DBN predicted lung cancer in an acceptable manner, with an average F-measure value of  0. 96 and an average Matthews Correlation Coefficient (MCC) value of 0. 47 for 6 folds.

scholarly journals Pipeline Leak Aperture Recognition Based on Wavelet Packet Analysis and a Deep Belief Network with ICR

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Xianming Lang ◽  
Zhiyong Hu ◽  
Ping Li ◽  
Yan Li ◽  
Jiangtao Cao ◽  
...  
Keyword(s):  
Sound Velocity ◽  
Wavelet Packet ◽  
Ultrasonic Signal ◽  
Deep Belief Network ◽  
Independent Component ◽  
Fine Tuning ◽  
Local Optimum ◽  
Wavelet Packet Analysis ◽  
Belief Network ◽  
Oil Pipeline

The leakage aperture cannot be easily identified, when an oil pipeline has small leaks. To address this issue, a leak aperture recognition method based on wavelet packet analysis (WPA) and a deep belief network (DBN) with independent component regression (ICR) is proposed. WPA is used to remove the noise in the collected sound velocity of the ultrasonic signal. Next, the denoised sound velocity of the ultrasonic signal is input into the deep belief network with independent component regression (DBNICR) to recognize different leak apertures. Because the optimization of the weights of the DBN with the gradient leads to a local optimum and a slow learning rate, ICR is used to replace the gradient fine-tuning method in conventional DBN for improving the classification accuracy, and a Lyapunov function is constructed to prove the convergence of the DBNICR learning process. By analyzing the acquired ultrasonic sound velocity of different leak apertures, the results show that the proposed method can quickly and effectively identify different leakage apertures.

scholarly journals Single and combined fault diagnosis of reciprocating compressor valves using a hybrid deep belief network

2017 ◽  
Vol 232 (20) ◽  
pp. 3767-3780 ◽  
Author(s):  
Van Tung Tran ◽  
Faisal AlThobiani ◽  
Tiedo Tinga ◽  
Andrew Ball ◽  
Gang Niu
Keyword(s):  
Receptive Fields ◽  
Deep Belief Network ◽  
Restricted Boltzmann Machine ◽  
Fault Classification ◽  
Fuzzy Artmap ◽  
Training Procedure ◽  
Reciprocating Compressor ◽  
Boltzmann Machine ◽  
Restricted Boltzmann Machines ◽  
Belief Network

In this paper, a hybrid deep belief network is proposed to diagnose single and combined faults of suction and discharge valves in a reciprocating compressor. This hybrid integrates the deep belief network structured by multiple stacked restricted Boltzmann machines for pre-training and simplified fuzzy ARTMAP (SFAM) for fault classification. In the pre-training procedure, an algorithm for selecting local receptive fields is used to group the most similar features into the receptive fields of which top values are the units of each layer, and then restricted Boltzmann machine is applied to these units to construct a network. Unsupervised learning is also carried out for each restricted Boltzmann machine layer in this procedure to compute the network weights and biases. Finally, the network output is fed into SFAM to perform fault classification. In order to diagnose the valve faults, three signal types of vibration, pressure, and current are acquired from a two-stage reciprocating air compressor under different valve conditions such as suction leakages, discharge leakages, spring deterioration, and their combination. These signals are subsequently processed so that the useful fault information from the signals can be revealed; next, statistical features in the time and frequency domains are extracted from the signals and used as the inputs for hybrid deep belief network. Performance of hybrid deep belief network in fault classification is compared with that of the original deep belief network and the deep belief network combined with generalized discriminant analysis, where softmax regression is used as a classifier for the latter two models. The results indicate that hybrid deep belief network is more capable of improving the diagnosis accuracy and is feasible in industrial applications.

scholarly journals Model Predictive Control of Robotic Grinding Based on Deep Belief Network

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-12
Author(s):  
Shouyan Chen ◽  
Tie Zhang ◽  
Yanbiao Zou ◽  
Meng Xiao
Keyword(s):  
Feedback Control ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Model Simulation ◽  
Deep Belief Network ◽  
Model Parameters ◽  
Feed Forward ◽  
Belief Network ◽  
Control Approach ◽  
Robotic Grinding

Considering the influence of rigid-flexible dynamics on robotic grinding process, a model predictive control approach based on deep belief network (DBN) is proposed to control robotic grinding deformation. The rigid-flexible coupling dynamics of robotic grinding is first established, on the basis of which a robotic grinding prediction model is constructed to predict the change of robotic grinding status and perform feed-forward control. A rolling optimization formula derived from the energy function is also established to optimize control output in real time and perform feedback control. As the accurately model parameters are hard to obtain, a deep belief network is constructed to obtain the parameters of robotic grinding predictive model. Simulation and experimental results indicate that the proposed model predictive control approach can predict abrupt change of robotic grinding status caused by deformation and perform a feed-forward and feedback based combination control, reducing control overflow and system oscillation caused by inaccurate feedback control.

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