A Hybrid Recurrent Neural Network/Dynamic Probabilistic Graphical Model Predictor of the Disulfide Bonding State of Cysteines from the Primary Structure of Proteins

2016 ◽  
pp. 257-268 ◽  
Author(s):  
Marco Bongini ◽  
Vincenzo Laveglia ◽  
Edmondo Trentin
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Primary Structure ◽  
Graphical Model ◽  
Network Dynamic ◽  
Probabilistic Graphical Model ◽  
Disulfide Bonding ◽  
Neural Network Dynamic ◽  
Structure Of Proteins ◽  
Model Predictor

scholarly journals Adaptive neural network dynamic surface control for musculoskeletal robots

2014 ◽  
Author(s):  
Michael Jantsch ◽  
Steffen Wittmeier ◽  
Konstantinos Dalamagkidis ◽  
Guido Herrmann ◽  
Alois Knoll
Keyword(s):  
Neural Network ◽  
Dynamic Surface Control ◽  
Network Dynamic ◽  
Adaptive Neural Network ◽  
Dynamic Surface ◽  
Surface Control ◽  
Neural Network Dynamic

scholarly journals Neural networks-based robust adaptive flight path tracking control of large transport

2018 ◽  
Vol 38 (3) ◽  
pp. 268-278
Author(s):  
Maolong Lv ◽  
Xiuxia Sun ◽  
G. Z. Xu ◽  
Z. T. Wang
Keyword(s):  
Neural Network ◽  
Control Method ◽  
Nonlinear Modeling ◽  
Dynamic Surface Control ◽  
Continuous Functions ◽  
Network Dynamic ◽  
Dynamic Surface ◽  
The Neural Network ◽  
Neural Network Dynamic ◽  
Robust Adaptive

For the ultralow altitude airdrop decline stage, many factors such as actuator nonlinearity, the uncertain atmospheric disturbances, and model unknown nonlinearity affect the precision of trajectory tracking. A robust adaptive neural network dynamic surface control method is proposed. The neural network is used to approximate unknown nonlinear continuous functions of the model, and a nonlinear robust term is introduced to eliminate the actuator’s nonlinear modeling error and external disturbances. From Lyapunov stability theorem, it is rigorously proved that all the signals in the closed-loop system are bounded. Simulation results confirm the perfect tracking performance and strong robustness of the proposed method.

scholarly journals How groundwater level can predict under the effect of climate change by using artificial neural networks of NARX

2020 ◽  
Vol 2 (1) ◽  
pp. 90-99
Author(s):  
Safieh Javadinejad ◽  
◽  
Rebwar Dara ◽  
Forough Jafary ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Climate Change ◽  
Groundwater Resources ◽  
Neural Network Modeling ◽  
Network Dynamic ◽  
Software Environment ◽  
Groundwater Levels ◽  
Current State ◽  
Neural Network Dynamic ◽  
The Status

The phenomenon of climate change in recent years has led to significant changes in climatic elements and as a result the status of surface and groundwater resources, especially in arid and semi-arid regions, this issue has sometimes caused a significant decline in groundwater resources. In this paper, the effects of climate change on the status of groundwater resources in Marvdasht plain have been investigated. Water supply of different parts of this region is highly dependent on groundwater resources and therefore the study of groundwater changes in future periods is important in the development of this plain and the management of its water resources. In order to evaluate the effects of climate change, the output of atmospheric circulation models (GCM) has been used. Then, in order to adapt the output scale of these models to the scale required by local studies of climate change, precipitation and temperature data have been downscaled by LARS-WG model. Downscaled information was used to determine the amount of feed and drainage of the aquifer in future periods. To investigate changes in groundwater levels at different stages, a neural network dynamic model has been developed in MATLAB software environment. It is also possible to study and compare other points using other scenarios and mathematical modeling. The results of the study, assuming the current state of development in the region, indicate a downward trend in the volume of the aquifer due to climate change and its effects on resources and uses of the study area. The results also introduce Scenario A2 as the most critical scenario related to climate change, which also shows the largest aquifer decline in neural network modeling.

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