scholarly journals Neural-Network Enhanced Visualization of High-Dimensional Data

10.5772/9165 ◽  
2010 ◽  
Author(s):  
Urska Cvek ◽  
Marjan Trutschl ◽  
John Cliffor
Keyword(s):  
Neural Network ◽  
High Dimensional Data ◽  
High Dimensional

scholarly journals A Surrogate Model Based on Artificial Neural Network for RF Radiation Modelling with High-Dimensional Data

2020 ◽  
Vol 17 (7) ◽  
pp. 2586 ◽  
Author(s):  
Xi Cheng ◽  
Clément Henry ◽  
Francesco P. Andriulli ◽  
Christian Person ◽  
Joe Wiart
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Uncertainty Quantification ◽  
Network Architecture ◽  
High Dimensional Data ◽  
High Dimensional ◽  
Attractive Alternative ◽  
Neural Network Architecture ◽  
Radiation Modelling ◽  
Artificial Neural

This paper focuses on quantifying the uncertainty in the specific absorption rate values of the brain induced by the uncertain positions of the electroencephalography electrodes placed on the patient’s scalp. To avoid running a large number of simulations, an artificial neural network architecture for uncertainty quantification involving high-dimensional data is proposed in this paper. The proposed method is demonstrated to be an attractive alternative to conventional uncertainty quantification methods because of its considerable advantage in the computational expense and speed.

scholarly journals Instance-Wise Denoising Autoencoder for High Dimensional Data

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Lin Chen ◽  
Wan-Yu Deng
Keyword(s):  
Neural Network ◽  
High Dimensional Data ◽  
Information Loss ◽  
Feature Representation ◽  
High Dimensional ◽  
Denoising Autoencoder ◽  
Text Data ◽  
Efficiency And Effectiveness ◽  
Zero Vector ◽  
Heterogeneous Feature

Denoising Autoencoder (DAE) is one of the most popular fashions that has reported significant success in recent neural network research. To be specific, DAE randomly corrupts some features of the data to zero as to utilize the cooccurrence information while avoiding overfitting. However, existing DAE approaches do not fare well on sparse and high dimensional data. In this paper, we present a Denoising Autoencoder labeled here as Instance-Wise Denoising Autoencoder (IDA), which is designed to work with high dimensional and sparse data by utilizing the instance-wise cooccurrence relation instead of the feature-wise one. IDA works ahead based on the following corruption rule: if an instance vector of nonzero feature is selected, it is forced to become a zero vector. To avoid serious information loss in the event that too many instances are discarded, an ensemble of multiple independent autoencoders built on different corrupted versions of the data is considered. Extensive experimental results on high dimensional and sparse text data show the superiority of IDA in efficiency and effectiveness. IDA is also experimented on the heterogenous transfer learning setting and cross-modal retrieval to study its generality on heterogeneous feature representation.

scholarly journals A Formation Flight Method with an Improved Deep Neural Network for Multi-UAV System

2020 ◽  
Vol 38 (2) ◽  
pp. 295-302
Author(s):  
Wenguang Xie ◽  
Kang Wu ◽  
Fang Yan ◽  
Haobin Shi ◽  
Xiaocheng Zhang
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Pid Controller ◽  
Deep Neural Network ◽  
High Dimensional Data ◽  
Formation Flight ◽  
High Dimensional ◽  
Electronic Warfare ◽  
The Real ◽  
Multi Uav

It is crucial to develop an effective controller for the multi-UAV system to contribute to the frontier fields, such as the electronic warfare. To address the dilemma of the cooperative formation with the high dimensional data, a deep neural network(NN) controller is developed in this paper. Firstly, a deep NN model is used to tune parameters of PID controller online. Secondly, this paper introduces an improved deep NN model integrating the momentum to improve the performance of the classical NN model and satisfy the condition for the real time cooperative formation. Lastly, the cooperative formation task is achieved by extending the proposed cooperative controller with an improved NN to the complex multi-UAV system. The simulation result of multi-UAV formation demonstrates the effectiveness of the proposed method, which achieves a faster formation than competitors.

scholarly journals Industrial Big Data and Computational Sustainability: Multi-Method Comparison Driven by High-Dimensional Data for Improving Reliability and Sustainability of Complex Systems

2019 ◽  
Vol 11 (17) ◽  
pp. 4557 ◽  
Author(s):  
Chunting Liu ◽  
Guozhu Jia
Keyword(s):  
Neural Network ◽  
Sustainable Development ◽  
Fault Diagnosis ◽  
Complex Systems ◽  
High Dimensional Data ◽  
Method Comparison ◽  
Data Driven ◽  
Superior Performance ◽  
High Dimensional ◽  
Computational Sustainability

Sustainable development is of great significance. The emerging research on data-driven computational sustainability has become an effective way to solve this problem. This paper presents a fault diagnosis and prediction framework for complex systems based on multi-dimensional data and multi-method comparison, aimed at improving the reliability and sustainability of the system by selecting methods with relatively superior performance. This study took the avionics system in the industrial field as an example. Based on the literature research on typical fault modes and fault diagnosis requirements of avionics systems, three popular high-dimensional data-driven fault diagnosis methods—support vector machine, convolutional neural network, and long- and short-term memory neural network—were comprehensively analyzed and compared. Finally, the actual bearing failure data were used for programming in order to verify and compare various methods and the process of selecting the superior method driven by high-dimensional data was fully demonstrated. We attempt to provide a sustainable development idea that continuously explores multi-method integration and comparison, aimed at improving the calculation efficiency and accuracy of reliability assessments, optimizing system performance, and ultimately achieving the goal of long-term improvement of system reliability and sustainability.

scholarly journals Inferring a network from dynamical signals at its nodes

2020 ◽  
Vol 16 (11) ◽  
pp. e1008435
Author(s):  
Corey Weistuch ◽  
Luca Agozzino ◽  
Lilianne R. Mujica-Parodi ◽  
Ken A. Dill
Keyword(s):  
Neural Network ◽  
Inverse Problem ◽  
Approximate Solution ◽  
High Dimensional Data ◽  
Time Dependent ◽  
High Dimensional ◽  
Toggle Switch ◽  
The Brain ◽  
Switch Circuit

We give an approximate solution to the difficult inverse problem of inferring the topology of an unknown network from given time-dependent signals at the nodes. For example, we measure signals from individual neurons in the brain, and infer how they are inter-connected. We use Maximum Caliber as an inference principle. The combinatorial challenge of high-dimensional data is handled using two different approximations to the pairwise couplings. We show two proofs of principle: in a nonlinear genetic toggle switch circuit, and in a toy neural network.

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