Neural network for black-box fusion of underwater robot localization under unmodeled noise

2018 ◽  
Vol 110 ◽  
pp. 57-72 ◽  
Author(s):  
Hendry Ferreira Chame ◽  
Matheus Machado dos Santos ◽  
Silvia Silva da Costa Botelho
Keyword(s):  
Neural Network ◽  
Black Box ◽  
Underwater Robot ◽  
Robot Localization

scholarly journals Opening the black box of neural networks: methods for interpreting neural network models in clinical applications

2018 ◽  
Vol 6 (11) ◽  
pp. 216-216 ◽  
Author(s):  
Zhongheng Zhang ◽  
◽  
Marcus W. Beck ◽  
David A. Winkler ◽  
Bin Huang ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Models ◽  
Black Box ◽  
Clinical Applications ◽  
Neural Network Models

scholarly journals Experiments with Underwater Robot Localization and Tracking

2007 ◽  
Author(s):  
Peter Corke ◽  
Carrick Detweiler ◽  
Matthew Dunbabin ◽  
Michael Hamilton ◽  
Daniela Rus ◽  
...  
Keyword(s):  
Underwater Robot ◽  
Robot Localization ◽  
Localization And Tracking

A comparison of the ability of black box and neural network models of ARX structure to represent a fluidized bed anaerobic digestion process

1999 ◽  
Vol 33 (4) ◽  
pp. 1027-1037 ◽  
Author(s):  
G.C. Premier ◽  
R. Dinsdale ◽  
A.J. Guwy ◽  
F.R. Hawkes ◽  
D.L. Hawkes ◽  
...  
Keyword(s):  
Neural Network ◽  
Anaerobic Digestion ◽  
Fluidized Bed ◽  
Network Models ◽  
Black Box ◽  
Neural Network Models ◽  
Anaerobic Digestion Process ◽  
Digestion Process

Compressing Deep Neural Network: A Black-Box System Identification Approach

2021 ◽  
Author(s):  
Ishan Sahu ◽  
Arpan Pal ◽  
Arijit Ukil ◽  
Angshul Majumdar
Keyword(s):  
Neural Network ◽  
System Identification ◽  
Deep Neural Network ◽  
Black Box ◽  
Identification Approach

scholarly journals Probabilistic Jacobian-Based Saliency Maps Attacks

2020 ◽  
Vol 2 (4) ◽  
pp. 558-578
Author(s):  
Théo Combey ◽  
António Loison ◽  
Maxime Faucher ◽  
Hatem Hajri
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Saliency Map ◽  
Black Box ◽  
Saliency Maps ◽  
Trade Offs ◽  
Good Trade ◽  
Neural Network Classifiers

Neural network classifiers (NNCs) are known to be vulnerable to malicious adversarial perturbations of inputs including those modifying a small fraction of the input features named sparse or L0 attacks. Effective and fast L0 attacks, such as the widely used Jacobian-based Saliency Map Attack (JSMA) are practical to fool NNCs but also to improve their robustness. In this paper, we show that penalising saliency maps of JSMA by the output probabilities and the input features of the NNC leads to more powerful attack algorithms that better take into account each input’s characteristics. This leads us to introduce improved versions of JSMA, named Weighted JSMA (WJSMA) and Taylor JSMA (TJSMA), and demonstrate through a variety of white-box and black-box experiments on three different datasets (MNIST, CIFAR-10 and GTSRB), that they are both significantly faster and more efficient than the original targeted and non-targeted versions of JSMA. Experiments also demonstrate, in some cases, very competitive results of our attacks in comparison with the Carlini-Wagner (CW) L0 attack, while remaining, like JSMA, significantly faster (WJSMA and TJSMA are more than 50 times faster than CW L0 on CIFAR-10). Therefore, our new attacks provide good trade-offs between JSMA and CW for L0 real-time adversarial testing on datasets such as the ones previously cited.

UNDERWATER ROBOT INTELLIGENT CONTROL BASED ON MULTILAYER NEURAL NETWORK

2010 ◽  
Vol 43 (20) ◽  
pp. 179-183
Author(s):  
Alexander A. Dyda ◽  
Dmitry A. Os'kin
Keyword(s):  
Neural Network ◽  
Intelligent Control ◽  
Underwater Robot

scholarly journals Black-Box Modelling of a DC-DC Buck Converter Based on a Recurrent Neural Network

2020 ◽  
Author(s):  
Gabriel Rojas-Duenas ◽  
Jordi-Roger Riba ◽  
Khaled Kahalerras ◽  
Manuel Moreno-Eguilaz ◽  
Akash Kadechkar ◽  
...  
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Black Box ◽  
Buck Converter
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