Microwave radiometric technique to retrieve vapor, liquid and ice. I. Development of a neural network-based inversion method

1997 ◽  
Vol 35 (2) ◽  
pp. 224-236 ◽  
Author(s):  
L. Li ◽  
J. Vivekanandan ◽  
C.H. Chan ◽  
Leung Tsang
Keyword(s):  
Neural Network ◽  
Inversion Method ◽  
Radiometric Technique ◽  
Vapor Liquid

A neural network-based inversion method of a feedback linearization controller applied to a hydraulic actuator

2021 ◽  
Vol 43 (5) ◽  
Author(s):  
Fábio Augusto Pires Borges ◽  
Eduardo André Perondi ◽  
Mauro André Barbosa Cunha ◽  
Mario Roland Sobczyk
Keyword(s):  
Neural Network ◽  
Feedback Linearization ◽  
Inversion Method ◽  
Hydraulic Actuator

WAVEFORM INVERSION AND MULTI-LAYER NEURAL NETWORK

1995 ◽  
Vol 03 (03) ◽  
pp. 175-202 ◽  
Author(s):  
DONGYU FEI ◽  
JOHN T. KUO ◽  
YU-CHIUNG TENG
Keyword(s):  
Neural Network ◽  
Continuous Function ◽  
Mathematical Problem ◽  
Initial Guess ◽  
Acoustic Velocity ◽  
Inversion Method ◽  
True Parameter ◽  
Seismic Waveform ◽  
Neural Network Inversion ◽  
The Neural Network

This paper presents the concept of neural network inversion. The basic mathematical problem is to establish the mapping between two multi-dimensional spaces, addressing the continuous function mapping between two close intervals. It introduces the 3-layer neural network existence theorem and proves that the multi-layer neural network can approximate any continuous function in the sense of supernorm or mean-squares-norm, provided that the activation function is locally Riemann integrable and nonpolynomial. With an initial guess of the target parameter, which, in the present case, is acoustic velocity, in a prescribed sphere, which contains the true parameter, the neural network inversion method ensures the search reaching the global minima. The principle of the neural network inversion on the basis of the least-squares minimization (L2 norm) is developed. As its application, this method is employed to perform seismic waveform inversions — Model 1, for a homogeneous isotropic earth with a 2-D rectangle embedded body, and Model 2, for a layered earth with an elliptically elongated inclusion. A fast computation algorithm of the finite element method is adopted to generate a series of synthetic shot records for training the 3-layer neural network. The trained neural network possesses the capability to find the acoustic velocity of the embedded body in both Model 1 and 2 with a real-time solution within a sufficient accuracy.

Ü-Net: Deep-Learning Schemes for Ground Penetrating Radar Data Inversion

2020 ◽  
Vol 25 (2) ◽  
pp. 287-292
Author(s):  
Longhao Xie ◽  
Qing Zhao ◽  
Chunguang Ma ◽  
Binbin Liao ◽  
Jianjian Huo
Keyword(s):  
Neural Network ◽  
Dielectric Constant ◽  
Data Compression ◽  
Ground Penetrating Radar ◽  
Quantitative Imaging ◽  
Inversion Method ◽  
Output Unit ◽  
Data Inversion ◽  
Ground Penetrating ◽  
Constant Distribution

Electromagnetic (EM) inversion is a quantitative imaging technique that can describe the dielectric constant distribution of a target based on the EM signals scattered from it. In this paper, a novel deep neural network (DNN) based methodology for ground penetrating radar (GPR) data inversion, known as the Ü-net is introduced. The proposed Ü-net consists of three parts: a data compression unit, U-net, and an output unit. The novel inversion approach, based on supervised learning, uses a neural network to generate the dielectric constant distribution from GPR data. The GPR data can be compressed and reshaped the size using data compression unit. The U-net maps the object features to the dielectric constant distribution. The output unit meshes the dielectric constant distribution more finely. A novel feature of the proposed methodology is the application of instance normalization (IN) to the DNN EM inversion method and a comparison of its performance to batch normalization (BN). The validity of this technique is confirmed by numerical simulations. The Mean-Square Error of the test data sets is 0.087. These simulations prove that the instance normalization is suitable for GPR data inversion. The proposed approach is promising for achieving quality dielectric constant images in real-time.

scholarly journals Adaptive Neural Network-Based Satellite Attitude Control by Using the Dynamic Inversion Technique and a VSCMG Pyramidal Cluster

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-16 ◽  
Author(s):  
Mihai Lungu ◽  
Romulus Lungu
Keyword(s):  
Neural Network ◽  
Attitude Control ◽  
Approximation Error ◽  
Inversion Method ◽  
Dynamic Inversion ◽  
Control Law ◽  
Feed Forward Neural Network ◽  
Small Satellites ◽  
Control Moment ◽  
Satellite Attitude Control

The paper presents an adaptive system for the control of small satellites’ attitude by using a pyramidal cluster of four variable-speed control moment gyros as actuators. Starting from the dynamic model of the pyramidal cluster, an adaptive control law is designed by means of the dynamic inversion method and a feed-forward neural network-based nonlinear subsystem; the control law has a proportional-integrator component (for the control of the reduced-order linear subsystem) and an adaptive component (for the compensation of the approximation error associated with the function describing the dynamics of the nonlinear system). The software implementation and validation of the new control architecture are achieved by using the Matlab/Simulink environment.

scholarly journals Analysing 21cm signal with artificial neural network

2017 ◽  
Vol 12 (S333) ◽  
pp. 39-42
Author(s):  
Hayato Shimabukuro ◽  
Benoit Semelin
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Power Spectrum ◽  
Machine Learning Techniques ◽  
Inversion Method ◽  
Observation Data ◽  
The Neural Network ◽  
Learning Techniques ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

AbstractThe 21cm signal at epoch of reionization (EoR) should be observed within next decade. We expect that cosmic 21cm signal at the EoR provides us both cosmological and astrophysical information. In order to extract fruitful information from observation data, we need to develop inversion method. For such a method, we introduce artificial neural network (ANN) which is one of the machine learning techniques. We apply the ANN to inversion problem to constrain astrophysical parameters from 21cm power spectrum. We train the architecture of the neural network with 70 training datasets and apply it to 54 test datasets with different value of parameters. We find that the quality of the parameter reconstruction depends on the sensitivity of the power spectrum to the different parameter sets at a given redshift and also find that the accuracy of reconstruction is improved by increasing the number of given redshifts. We conclude that the ANN is viable inversion method whose main strength is that they require a sparse extrapolation of the parameter space and thus should be usable with full simulation.

Application of Hybrid Genetic Algorithm in Ground Stress Inversion

2011 ◽  
Vol 90-93 ◽  
pp. 337-341
Author(s):  
Ran Gang Yu ◽  
Yong Tian
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Neural Networks ◽  
Bp Neural Network ◽  
Local Minimum ◽  
Hybrid Genetic Algorithm ◽  
Inversion Method ◽  
Stress Inversion ◽  
Neural Network Inversion ◽  
Ground Stress

This paper propose genetic algorithm combined with neural networks, greatly improving the convergence rate of neural network aim at the disadvantage of the traditional BP neural network inversion method is easy to fall into local minimum and slow convergence.Finally, verified the feasibility and superiority of the above methods through the successful initial ground stress inversion of actual project.

Prediction of vapor–liquid equilibrium for binary systems containing HFEs by using artificial neural network

2002 ◽  
Vol 199 (1-2) ◽  
pp. 63-78 ◽  
Author(s):  
Shingo Urata ◽  
Akira Takada ◽  
Junji Murata ◽  
Toshihiko Hiaki ◽  
Akira Sekiya
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Binary Systems ◽  
Liquid Equilibrium ◽  
Vapor Liquid Equilibrium ◽  
Artificial Neural ◽  
Vapor Liquid
Sign in / Sign up

Export Citation Format

Share Document