Using a Recurrent Neural Network and Restricted Boltzmann Machines for Malicious Traffic Detection

2018 ◽  
Vol 16 (5) ◽  
Author(s):  
Chaopeng Li ◽  
Jinlin Wang ◽  
Xiaozhou Ye
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Restricted Boltzmann Machines ◽  
Boltzmann Machines ◽  
Traffic Detection

scholarly journals Adaptive hyperparameter updating for training restricted Boltzmann machines on quantum annealers

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Guanglei Xu ◽  
William S. Oates
Keyword(s):  
Neural Network ◽  
Maximum Likelihood ◽  
Image Reconstruction ◽  
Image Recognition ◽  
Shannon Entropy ◽  
Reconstruction Error ◽  
Likelihood Method ◽  
Restricted Boltzmann Machines ◽  
Boltzmann Machines ◽  
D Wave

AbstractRestricted Boltzmann Machines (RBMs) have been proposed for developing neural networks for a variety of unsupervised machine learning applications such as image recognition, drug discovery, and materials design. The Boltzmann probability distribution is used as a model to identify network parameters by optimizing the likelihood of predicting an output given hidden states trained on available data. Training such networks often requires sampling over a large probability space that must be approximated during gradient based optimization. Quantum annealing has been proposed as a means to search this space more efficiently which has been experimentally investigated on D-Wave hardware. D-Wave implementation requires selection of an effective inverse temperature or hyperparameter ($$\beta $$ β ) within the Boltzmann distribution which can strongly influence optimization. Here, we show how this parameter can be estimated as a hyperparameter applied to D-Wave hardware during neural network training by maximizing the likelihood or minimizing the Shannon entropy. We find both methods improve training RBMs based upon D-Wave hardware experimental validation on an image recognition problem. Neural network image reconstruction errors are evaluated using Bayesian uncertainty analysis which illustrate more than an order magnitude lower image reconstruction error using the maximum likelihood over manually optimizing the hyperparameter. The maximum likelihood method is also shown to out-perform minimizing the Shannon entropy for image reconstruction.

scholarly journals Learning a compass spin model with neural network quantum states

2021 ◽  
Author(s):  
Eric Zou ◽  
Erik Long ◽  
Erhai Zhao
Keyword(s):  
Neural Network ◽  
Ground States ◽  
Spin Model ◽  
Quantum Spin ◽  
Quantum States ◽  
Stochastic Gradient Descent ◽  
Tuning Parameter ◽  
Honeycomb Lattice ◽  
Restricted Boltzmann Machines ◽  
Boltzmann Machines

Abstract Neural network quantum states provide a novel representation of the many-body states of interacting quantum systems and open up a promising route to solve frustrated quantum spin models that evade other numerical approaches. Yet its capacity to describe complex magnetic orders with large unit cells has not been demonstrated, and its performance in a rugged energy landscape has been questioned. Here we apply restricted Boltzmann machines and stochastic gradient descent to seek the ground states of a compass spin model on the honeycomb lattice, which unifies the Kitaev model, Ising model and the quantum 120-degree model with a single tuning parameter. We report calculation results on the variational energy, order parameters and correlation functions. The phase diagram obtained is in good agreement with the predictions of tensor network ansatz, demonstrating the capacity of restricted Boltzmann machines in learning the ground states of frustrated quantum spin Hamiltonians. The limitations of the calculation are discussed. A few strategies are outlined to address some of the challenges in machine learning frustrated quantum magnets.

scholarly journals Anomaly Detection of Wind Turbines Based on Deep Small-World Neural Network

2020 ◽  
Vol 10 (4) ◽  
pp. 1243 ◽  
Author(s):  
Meng Li ◽  
Shuangxin Wang ◽  
Shanxiang Fang ◽  
Juchao Zhao
Keyword(s):  
Neural Network ◽  
Anomaly Detection ◽  
Wind Turbines ◽  
Small World ◽  
Value Theory ◽  
False Alarms ◽  
Learning Networks ◽  
Restricted Boltzmann Machines ◽  
Boltzmann Machines ◽  
Early Failures

Accurate and efficient condition monitoring is the key to enhance the reliability and security of wind turbines. In recent years, an intelligent anomaly detection method based on deep learning networks has been receiving increasing attention. Since accurately labeled data are usually difficult to obtain in real industries, this paper proposes a novel Deep Small-World Neural Network (DSWNN) on the basis of unsupervised learning to detect the early failures of wind turbines. During network construction, a regular auto-encoder network with multiple restricted Boltzmann machines is first constructed and pre-trained by using unlabeled data of wind turbines. After that, the trained network is transformed into a DSWNN model by randomly add-edges method, where the network parameters are fine-tuned by using minimal amounts of labeled data. In order to guard against the changes and disturbances of wind speed and reduce false alarms, an adaptive threshold based on extreme value theory is presented as the criterion of anomaly judgment. The DSWNN model is excellent in depth mining data characteristics and accurate measurement error. Last, two failure cases of wind turbine anomaly detection are given to demonstrate its validity and accuracy of the proposed methodology contrasted with the deep belief network and deep neural network.

Research on behavior recognition based on feature fusion of automatic coder and recurrent neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8927-8935
Author(s):  
Bing Zheng ◽  
Dawei Yun ◽  
Yan Liang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Behavior Pattern ◽  
Rapid Development ◽  
Video Data ◽  
Support Vector ◽  
Behavior Recognition ◽  
Learning Methods ◽  
The Impact ◽  
Internet Of Things Technology

Under the impact of COVID-19, research on behavior recognition are highly needed. In this paper, we combine the algorithm of self-adaptive coder and recurrent neural network to realize the research of behavior pattern recognition. At present, most of the research of human behavior recognition is focused on the video data, which is based on the video number. At the same time, due to the complexity of video image data, it is easy to violate personal privacy. With the rapid development of Internet of things technology, it has attracted the attention of a large number of experts and scholars. Researchers have tried to use many machine learning methods, such as random forest, support vector machine and other shallow learning methods, which perform well in the laboratory environment, but there is still a long way to go from practical application. In this paper, a recursive neural network algorithm based on long and short term memory (LSTM) is proposed to realize the recognition of behavior patterns, so as to improve the accuracy of human activity behavior recognition.

Deadlift Recognition and Application based on Multiple Modalities using Recurrent Neural Network

2020 ◽  
Vol 2020 (17) ◽  
pp. 2-1-2-6
Author(s):  
Shih-Wei Sun ◽  
Ting-Chen Mou ◽  
Pao-Chi Chang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Network Structure ◽  
Inertial Sensors ◽  
Body Movement ◽  
The Body ◽  
Body Parts ◽  
Multimodal Sensing ◽  
Multiple Devices ◽  
Neural Network Structure

To improve the workout efficiency and to provide the body movement suggestions to users in a “smart gym” environment, we propose to use a depth camera for capturing a user’s body parts and mount multiple inertial sensors on the body parts of a user to generate deadlift behavior models generated by a recurrent neural network structure. The contribution of this paper is trifold: 1) The multimodal sensing signals obtained from multiple devices are fused for generating the deadlift behavior classifiers, 2) the recurrent neural network structure can analyze the information from the synchronized skeletal and inertial sensing data, and 3) a Vaplab dataset is generated for evaluating the deadlift behaviors recognizing capability in the proposed method.

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