An Adaptive Deep Belief Network With Sparse Restricted Boltzmann Machines

Wang, Gongming; Qiao, Junfei; Bi, Jing; Jia, Qing‐Shan; Zhou, MengChu

doi:10.1109/tnnls.2019.2952864

Cited by 52 publications

(19 citation statements)

References 41 publications

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“…This machine is stacked with RBM, which has powerful feature extraction capabilities. The restricted Boltzmann machine [28][29] is a Markov random field model, which has a 2-layer structure, as shown in Figure 1. The lower layer is the input layer, containing input units , used to represent input data, and each input unit contains a real-valued offset ; the upper layer is the hidden layer, containing hidden units ℎ , which represent the input extracted by RBM abstract feature of data, each hidden unit contains a real-valued bias .…”

Section: A Deep Belief Networkmentioning

confidence: 99%

“…In order to highlight the powerful non-linear fitting ability, Since the amount of source training data is relatively large relative to the amount of task data, and the amount of data to be calculated is small, in order to test whether the amount of task data affects the migration result when the source training data is migrated based on the maximum mean difference contribution coefficient method, auxiliary sample data is introduced and set are 10 auxiliary sample batches, and the data of August 22, August (22)(23), August (22)(23)(24),..., August (22)(23)(24)(25)(26)(27)(28)(29)(30)(31) are taken as 10 sample data, the number of samples is 96,192,...,960 in sequence. Then take the data under different auxiliary samples as the target data, migrate data close to the target data distribution from the source data, calculate the MMD value of each auxiliary sample, the source data, and the migrated data respectively, and use the migration data of each auxiliary sample The TDBN-DNN model obtained after finetuning the network calculates the target task data, and the Gaussian kernel width control parameter = 2.…”

Section: Comparison Of Dbn-dnn and Tdbn-dnn Algorithmsmentioning

confidence: 99%

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An Efficient Scheme for Determining the Power Loss in Wind-PV Based on Deep Learning

et al. 2021

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Section: A Deep Belief Networkmentioning

confidence: 99%

Section: Comparison Of Dbn-dnn and Tdbn-dnn Algorithmsmentioning

confidence: 99%

An Efficient Scheme for Determining the Power Loss in Wind-PV Based on Deep Learning

et al. 2021

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“…IDBN-WSVM includes a range of superimposed RBMs [28] and a WSVM layer ( Figure 2). The training process of IDBN-WSVM includes two steps.…”

Section: Proposed Models and Processes A Construction And Trainmentioning

confidence: 99%

Large-Scale and Robust Intrusion Detection Model Combining Improved Deep Belief Network With Feature-Weighted SVM

Lee

et al. 2020

IEEE Access

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In a large-scale data environment, the ''curse of dimensionality'' of high-dimensional feature spaces and the large amount of noisy data make the efficiency and accuracy of intrusion detection systems (IDSs) significantly decrease. To address these challenges, the underlying algorithm can not only reduce dimensionality, but also remove some redundant and irrelevant noise data from the massive data. Accordingly, herein, an IDS combining deep belief network (DBN) with feature-weighted support vector machines (WSVM) is proposed. First, an adaptive learning rate strategy is applied to promote the training performance of the IDBN, which is used for learning deep features from raw data for reducing dimensionality. Second, the particle swarm optimization algorithm is used to optimize the SVM, followed by the determination of the weights of deep features and the best parameters of the Gaussian kernel, resulting in WSVM which can remove weakly related and redundant features from all IDBN-extracted features. The NSL-KDD dataset was used to validate the IDBN-WSVM model. In particular, the model performance was studied and compared to a model comprising a non-weighted SVM and other machine learning methods. Experimental results demonstrate that IDBN-WSVM is well-suited for designing high-precision classification models. The proposed improved model achieves accuracies of 85.73% and 82.36% in binary-and five-category classification experiments, respectively, which is better than or near state-of-the-art method. The IDBN-WSVM model not only saves training time and testing time on large-scale datasets, but also is more robust and has better performance of generalization than traditional methods, which provides a new research method that achieves high accuracy in intrusion detection tasks.

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“…Robustness and adaptability are important indicators to measure the pros and cons of algorithms [11], [12]. Existing map inference algorithms with trajectories have been able to construct relatively complete road networks after researching for a decade.…”

Section: Introductionmentioning

confidence: 99%

Density Adaptive Approach for Generating Road Network From GPS Trajectories

et al. 2020

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Road networks are fundamental parts of intelligent transportation and smart cities. With the emergence of crowdsourcing geographic data, road mapping approaches by using crowdsourcing trajectories have been developed. Existing road map inference algorithms from trajectories can extract relatively accurate road networks, however, these algorithms are not robust to different trajectory datasets and the parameter optimization task is tedious and time-consuming. Therefore, we propose an adaptive approach based on trajectory density. The proposed approach contains two stages. Firstly, the density distribution for each trajectory is adaptively estimated by the Gaussian fitting approach and the density peak points are extracted to construct road centerlines corresponding to each trajectory. Secondly, these extracted road centerlines are incrementally merged by the ''matching-refinement-merging'' process to generate a road network. We compare the proposed approach against four representative methods through trajectory datasets that are completely different in sampling frequency, trajectory density, road density, and noise. The results show that the proposed approach provides better accuracy in terms of precision and integrity and does not require additional parameter adjustment.

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An Adaptive Deep Belief Network With Sparse Restricted Boltzmann Machines

Cited by 52 publications

References 41 publications

An Efficient Scheme for Determining the Power Loss in Wind-PV Based on Deep Learning

An Efficient Scheme for Determining the Power Loss in Wind-PV Based on Deep Learning

Large-Scale and Robust Intrusion Detection Model Combining Improved Deep Belief Network With Feature-Weighted SVM

Density Adaptive Approach for Generating Road Network From GPS Trajectories

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