scholarly journals Network-based Observability and Controllability Analysis of Dynamical Systems: the NOCAD toolbox

F1000Research ◽  
2019 ◽  
Vol 8 ◽  
pp. 646
Author(s):  
Dániel Leitold ◽  
Ágnes Vathy-Fogarassy ◽  
János Abonyi
Keyword(s):  
Neural Network ◽  
Caenorhabditis Elegans ◽  
Dynamical Systems ◽  
Network Science ◽  
Sensor Placement ◽  
Life Sciences ◽  
The Neural Network ◽  
Structural Controllability ◽  
Controllability And Observability

The network science-based determination of driver nodes and sensor placement has become increasingly popular in the field of dynamical systems over the last decade. In this paper, the applicability of the methodology in the field of life sciences is introduced through the analysis of the neural network of Caenorhabditis elegans. Simultaneously, an Octave and MATLAB-compatible NOCAD toolbox is proposed that provides a set of methods to automatically generate the relevant structural controllability and observability associated measures for linear or linearised systems and compare the different sensor placement methods.

scholarly journals Network-based Observability and Controllability Analysis of Dynamical Systems: the NOCAD toolbox

F1000Research ◽  
2019 ◽  
Vol 8 ◽  
pp. 646 ◽  
Author(s):  
Dániel Leitold ◽  
Ágnes Vathy-Fogarassy ◽  
János Abonyi
Keyword(s):  
Dynamical Systems ◽  
Network Science ◽  
Life Science ◽  
Observability Analysis ◽  
Structural Controllability ◽  
Controllability And Observability

Network science has become increasingly important in life science over the last decade. The proposed Octave and MATLAB-compatible NOCAD toolbox provides a set of methods which enables the structural controllability and observability analysis of dynamical systems. In this paper, the functionality of the toolbox is presented, and the implemented functions demonstrated.

Dynamics of Neural Networks as Nonlinear Systems with Several Equilibria

2011 ◽  
pp. 331-357 ◽  
Author(s):  
Daniela Danciu
Keyword(s):  
Neural Network ◽  
Dynamical System ◽  
Neural Networks ◽  
Time Delay ◽  
Dynamical Systems ◽  
Qualitative Properties ◽  
The Neural Network ◽  
The Neural Networks ◽  
Global Asymptotics ◽  
Dynamical Properties

Neural networks—both natural and artificial, are characterized by two kinds of dynamics. The first one is concerned with what we would call “learning dynamics”. The second one is the intrinsic dynamics of the neural network viewed as a dynamical system after the weights have been established via learning. The chapter deals with the second kind of dynamics. More precisely, since the emergent computational capabilities of a recurrent neural network can be achieved provided it has suitable dynamical properties when viewed as a system with several equilibria, the chapter deals with those qualitative properties connected to the achievement of such dynamical properties as global asymptotics and gradient-like behavior. In the case of the neural networks with delays, these aspects are reformulated in accordance with the state of the art of the theory of time delay dynamical systems.

Synthesis, spectral description, and lipophilicity parameters determination of phenylcarbamic acid derivatives with integrated N-phenylpiperazine moiety in the structure

2006 ◽  
Vol 60 (1) ◽  
Author(s):  
I. Malík ◽  
E. Sedlárová ◽  
J. Csöllei ◽  
F. Andriamainty ◽  
P. Kurfürst ◽  
...  
Keyword(s):  
Neural Network ◽  
Spectral Data ◽  
1H Nmr ◽  
Elemental Analysis ◽  
Computer Programs ◽  
Mass Spectral Data ◽  
Mass Spectral ◽  
The Neural Network ◽  
Lipophilicity Parameters

AbstractThe phenylcarbamic acid derivatives with N-phenylpiperazine moiety in the molecule have been prepared. The structure has been confirmed by elemental analysis, IR, 1H NMR, and mass spectral data. For the prepared set of the compounds the lipophilicity parameters have been determined. The experimentally obtained lipophilicity parameters have been correlated with theoretical entries obtained by different computer programs based on the neural network and fragmental methods.

Studies of Synchronous Rotor Gear Monitoring Technique Based on Ant Colony Neural Network

2011 ◽  
Vol 121-126 ◽  
pp. 382-386
Author(s):  
Yi Jun Chen ◽  
Qing Hai Zhao
Keyword(s):  
Neural Network ◽  
Ant Colony Algorithm ◽  
Accurate Determination ◽  
Ant Colony ◽  
Local Minima ◽  
Characteristic Parameters ◽  
Monitoring Technique ◽  
The Neural Network ◽  
Failure Types

In this paper, the nonlinear mapping relationship between characteristic parameters of failures and failure types is realized by using neural network through extracting characteristic variables of failures during operation of the gear. Aiming at the problems of neutral network such as slow convergence speed and existence of local minima, the neural network is optimized and the ant colony neural network is established by using the ant colony algorithm to realize rapid and accurate determination of failure status of a gear from characteristic parameters of failures. In addition, validity of the established model is verified through experiments.

Determination of evidence correction factors based on the neural network

2017 ◽  
Vol 34 (2) ◽  
pp. e12192 ◽  
Author(s):  
Weidong Zhu ◽  
Youhua Xu ◽  
Yong Wu ◽  
Yibo Sun
Keyword(s):  
Neural Network ◽  
Correction Factors ◽  
The Neural Network

scholarly journals Permeability Determination of Tertiary Reservoir/Ajeel Oil Field

2020 ◽  
Vol 26 (7) ◽  
pp. 206-216
Author(s):  
Rihab Abbass Deabl ◽  
Ahmad A. Ramadhan ◽  
AbdulAali A. Aldabaj
Keyword(s):  
Neural Network ◽  
Economic Value ◽  
Oil Field ◽  
The Other ◽  
Log Data ◽  
Neural Network Method ◽  
Core Data ◽  
The Neural Network ◽  
Network Method

This paper discusses the method for determining the permeability values of Tertiary Reservoir in Ajeel field (Jeribe, dhiban, Euphrates) units and this study is very important to determine the permeability values that it is needed to detect the economic value of oil in Tertiary Formation. This study based on core data from nine wells and log data from twelve wells. The wells are AJ-1, AJ-4, AJ-6, AJ-7, AJ-10, AJ-12, AJ-13, AJ-14, AJ-15, AJ-22, AJ-25, and AJ-54, but we have chosen three wells (AJ4, AJ6, and AJ10) to study in this paper. Three methods are used for this work and this study indicates that one of the best way of obtaining permeability is the Neural network method because the values of permeability obtained being much closer to the values of K-core than the other methods. From this study we obtained many values of permeability for all depths from top to bottom for three wells in Ajeel Field as explained by figures below.

scholarly journals Technical Note: Determination of aerosol optical properties by a calibrated sky imager

2009 ◽  
Vol 9 (17) ◽  
pp. 6417-6427 ◽  
Author(s):  
A. Cazorla ◽  
J. E. Shields ◽  
M. E. Karr ◽  
F. J. Olmo ◽  
A. Burden ◽  
...  
Keyword(s):  
Neural Network ◽  
Optical Properties ◽  
Standard Method ◽  
Technical Note ◽  
Atmospheric Radiation ◽  
The Neural Network ◽  
Physical Laboratory ◽  
Atmospheric Radiation Measurement ◽  
Sky Imager

Abstract. The calibrated ground-based sky imager developed in the Marine Physical Laboratory, the Whole Sky Imager (WSI), has been tested with data from the Atmospheric Radiation Measurement Program (ARM) at the Southern Great Plain site (SGP) to determine optical properties of the atmospheric aerosol. Different neural network-based models calculate the aerosol optical depth (AOD) for three wavelengths using the radiance extracted from the principal plane of sky images from the WSI as input parameters. The models use data from a CIMEL CE318 photometer for training and validation and the wavelengths used correspond to the closest wavelengths in both instruments. The spectral dependency of the AOD, characterized by the Ångström exponent α in the interval 440–870 nm, is also derived using the standard AERONET procedure and also with a neural network-based model using the values obtained with a CIMEL CE318. The deviations between the WSI derived AOD and the AOD retrieved by AERONET are within the nominal uncertainty assigned to the AERONET AOD calculation (±0.01), in 80% of the cases. The explanation of data variance by the model is over 92% in all cases. In the case of α, the deviation is within the uncertainty assigned to the AERONET α (±0.1) in 50% of the cases for the standard method and 84% for the neural network-based model. The explanation of data variance by the model is 63% for the standard method and 77% for the neural network-based model.

scholarly journals Autonomous Orbit Determination for Lagrangian Navigation Satellite Based on Neural Network Based State Observer

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Youtao Gao ◽  
Tanran Zhao ◽  
Bingyu Jin ◽  
Junkang Chen ◽  
Bo Xu
Keyword(s):  
Neural Network ◽  
Orbit Determination ◽  
Transition Matrix ◽  
State Observer ◽  
Nonlinear Perturbations ◽  
The Neural Network ◽  
Autonomous Orbit Determination ◽  
Simulation Results ◽  
Libration Orbits

In order to improve the accuracy of the dynamical model used in the orbit determination of the Lagrangian navigation satellites, the nonlinear perturbations acting on Lagrangian navigation satellites are estimated by a neural network. A neural network based state observer is applied to autonomously determine the orbits of Lagrangian navigation satellites using only satellite-to-satellite range. This autonomous orbit determination method does not require linearizing the dynamical mode. There is no need to calculate the transition matrix. It is proved that three satellite-to-satellite ranges are needed using this method; therefore, the navigation constellation should include four Lagrangian navigation satellites at least. Four satellites orbiting on the collinear libration orbits are chosen to construct a constellation which is used to demonstrate the utility of this method. Simulation results illustrate that the stable error of autonomous orbit determination is about 10 m. The perturbation can be estimated by the neural network.

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