scholarly journals Fast probabilistic nonlinear petrophysical inversion

Geophysics ◽  
2011 ◽  
Vol 76 (2) ◽  
pp. E45-E58 ◽  
Author(s):  
Mohammad S. Shahraeeni ◽  
Andrew Curtis
Keyword(s):  
Neural Network ◽  
Inverse Problems ◽  
Probabilistic Method ◽  
Clay Content ◽  
P Wave ◽  
Model Parameters ◽  
A Posteriori ◽  
Nonlinear Inverse Problems ◽  
Mixture Density ◽  
The Neural Network

We have developed an extension of the mixture-density neural network as a computationally efficient probabilistic method to solve nonlinear inverse problems. In this method, any postinversion (a posteriori) joint probability density function (PDF) over the model parameters is represented by a weighted sum of multivariate Gaussian PDFs. A mixture-density neural network estimates the weights, mean vector, and covariance matrix of the Gaussians given any measured data set. In one study, we have jointly inverted compressional- and shear-wave velocity for the joint PDF of porosity, clay content, and water saturation in a synthetic, fluid-saturated, dispersed sand-shale system. Results show that if the method is applied appropriately, the joint PDF estimated by the neural network is comparable to the Monte Carlo sampled a posteriori solution of the inverse problem. However, the computational cost of training and using the neural network is much lower than inversion by sampling (more than a factor of 104 in this case and potentially a much larger factor for 3D seismic inversion). To analyze the performance of the method on real exploration geophysical data, we have jointly inverted P-wave impedance and Poisson’s ratio logs for the joint PDF of porosity and clay content. Results show that the posterior model PDF of porosity and clay content is a good estimate of actual porosity and clay-content log values. Although the results may vary from one field to another, this fast, probabilistic method of solving nonlinear inverse problems can be applied to invert well logs and large seismic data sets for petrophysical parameters in any field.

Effects of model discretization on the statistics of model parameters in nonlinear inverse problems

2007 ◽  
Vol 121 (5) ◽  
pp. 3125-3125
Author(s):  
Andrew A. Ganse ◽  
Robert I. Odom ◽  
Andrew A. Ganse ◽  
Robert I. Odom
Keyword(s):  
Inverse Problems ◽  
Model Parameters ◽  
Nonlinear Inverse Problems

Explainable Learning Based on Hyperparameters Optimization of Lightweight Spatiotemporal Models for Cellular Traffic Prediction

2021 ◽  
Author(s):  
Wei-Che Chien ◽  
Hsin-Hung Cho ◽  
Fan-Hsun Tseng ◽  
Shih-Yeh Chen
Keyword(s):  
Neural Network ◽  
Mobile Communications ◽  
Rapid Development ◽  
Mobile Network ◽  
Traffic Prediction ◽  
Model Parameters ◽  
Data Types ◽  
The Neural Network ◽  
Spatiotemporal Models ◽  
Cellular Traffic

Abstract The rapid development of the Internet of Things and multimedia applications has led to an exponential growth in mobile network traffic year by year. In order to meet the demand for large amounts of data transmission and solve the problem of insufficient spectrum resources, millimeter waves are adopted for 5G communication. For B5G/6G, effective use of spectrum resources is one of the key technologies for the development of mobile communications. Therefore, this study uses a lightweight neural network to predict cellular traffic based on regional data, considering the data types of temporal and spatial dependence at the same time. Furthermore, in order to optimize the prediction performance and reduce the number of parameters of the neural network, this study uses a meta-heuristic algorithm to adjust the hyperparameters and combines local and global explanations to interpret the improvement of traffic prediction. The local explanations show the adjustment results of a single hyperparameter, and global explanations show the correlation between different hyperparameters and their influence on the amount and accuracy of model parameters. The simulation results show that compared with adjustment strategies of the manual method and greedy algorithm, the proposed explainable learning method can effectively improve the accuracy of cellular traffic prediction, reduce the number of parameters and provide a reasonable explanation.

scholarly journals Classification of Plant Leaf Diseases Based on Improved Convolutional Neural Network

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4161 ◽  
Author(s):  
Hang ◽  
Zhang ◽  
Chen ◽  
Zhang ◽  
Wang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Convergence Time ◽  
Model Parameters ◽  
Data Set ◽  
Training Time ◽  
Plant Leaf ◽  
The Neural Network ◽  
Target Disease ◽  
Fully Connected

Plant leaf diseases are closely related to people's daily life. Due to the wide variety of diseases, it is not only time-consuming and labor-intensive to identify and classify diseases by artificial eyes, but also easy to be misidentified with having a high error rate. Therefore, we proposed a deep learning-based method to identify and classify plant leaf diseases. The proposed method can take the advantages of the neural network to extract the characteristics of diseased parts, and thus to classify target disease areas. To address the issues of long training convergence time and too-large model parameters, the traditional convolutional neural network was improved by combining a structure of inception module, a squeeze-and-excitation (SE) module and a global pooling layer to identify diseases. Through the Inception structure, the feature data of the convolutional layer were fused in multi-scales to improve the accuracy on the leaf disease dataset. Finally, the global average pooling layer was used instead of the fully connected layer to reduce the number of model parameters. Compared with some traditional convolutional neural networks, our model yielded better performance and achieved an accuracy of 91.7% on the test data set. At the same time, the number of model parameters and training time have also been greatly reduced. The experimental classification on plant leaf diseases indicated that our method is feasible and effective.

scholarly journals A Comparative Study of Neural Networks and Fuzzy Systems in Modeling of a Nonlinear Dynamic System

2011 ◽  
Vol 1 (1) ◽  
pp. 65-73 ◽  
Author(s):  
Metin DEMIRTAS ◽  
Musa ALCI
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fuzzy Systems ◽  
Performance Modeling ◽  
Nonlinear Dynamic System ◽  
Predictive Performance ◽  
Model Parameters ◽  
Data Set ◽  
The Neural Network ◽  
Practical Performance

The aim of this paper is to compare the neural network and fuzzy modeling approaches on a nonlinear system. We have taken Permanent Magnet Brushless Direct Current (PMBDC) motor data and have generated models using both approaches. The predictive performance of both methods was compared on the data set for model configurations.The paper describes the results of these tests and discusses the effects of changing model parameters on predictive and practical performance. Modeling sensitivity was used to compare for two methods. 

Neural Network Control of a Flexible Link Manipulator in Contact With a Compliant Environment

2010 ◽  
Author(s):  
Reza Sabzehgar ◽  
Mehrdad Moallem
Keyword(s):  
Neural Network ◽  
Contact Force ◽  
A Priori ◽  
Network Control ◽  
Model Parameters ◽  
Flexible Link ◽  
System A ◽  
Neural Network Controller ◽  
The Neural Network ◽  
Network Controller

A neural network controller for regulating the contact force of a flexible link manipulator in contact with a compliant environment is proposed in this paper. The dynamic model of a single-link flexible (SLF) manipulator is obtained using three rigid sub-links connected by two virtual springs. It is assumed that the length of each link is short enough to be considered as a rigid link. A neural network-based control strategy is then proposed to relax the a-priori knowledge of the model parameters of the flexible link manipulator. The weights of the neural network controller are adjusted to minimize the error between the actual contact force and desired force. To overcome the non-minimum phase characteristic of the system, a weighted term of input signal is added to controller’s cost function. Simulation results are presented to evaluate performance of the proposed controller.

scholarly journals Fast probabilistic petrophysical mapping of reservoirs from 3D seismic data

Geophysics ◽  
2012 ◽  
Vol 77 (3) ◽  
pp. O1-O19 ◽  
Author(s):  
Mohammad S. Shahraeeni ◽  
Andrew Curtis ◽  
Gabriel Chao
Keyword(s):  
Seismic Data ◽  
Water Saturation ◽  
Random Noise ◽  
Clay Content ◽  
Inversion Method ◽  
Model Parameters ◽  
Nonlinear Inversion ◽  
Desktop Computer ◽  
Data Set ◽  
Mixture Density

A fast probabilistic inversion method for 3D petrophysical property prediction from inverted prestack seismic data has been developed and tested on a real data set. The inversion objective is to estimate the joint probability density function (PDF) of model vectors consisting of porosity, clay content, and water saturation components at each point in the reservoir, from data vectors with compressional- and shear-wave-impedance components that are obtained from the inversion of seismic data. The proposed inversion method is based on mixture density network (MDN), which is trained by a given set of training samples, and provides an estimate of the joint posterior PDF’s of the model parameters for any given data point. This method is much more time and memory efficient than conventional nonlinear inversion methods. The training data set is constructed using nonlinear petrophysical forward relations and includes different sources of uncertainty in the inverse problem such as variations in effective pressure, bulk modulus and density of hydrocarbon, and random noise in recorded data. Results showed that the standard deviations of all model parameters were reduced after inversion, which shows that the inversion process provides information about all parameters. The reduction of uncertainty in water saturation was smaller than that for porosity or clay content; nevertheless the maximum of the a posteriori (MAP) of model PDF clearly showed the boundary between brine saturated and oil saturated rocks at wellbores. The MAP estimates of different model parameters show the lateral and vertical continuity of these boundaries. Errors in the MAP estimate of different model parameters can be reduced using more accurate petrophysical forward relations. This fast, probabilistic, nonlinear inversion method can be applied to invert large seismic cubes for petrophysical parameters on a standard desktop computer.

scholarly journals Using the neural network in clinical systems

2021 ◽  
Vol 2131 (4) ◽  
pp. 042008
Author(s):  
Yu S Gusynina ◽  
T A Shornikova
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Local Level ◽  
Probabilistic Method ◽  
Complex Function ◽  
Two Dimensions ◽  
Image Size ◽  
Network Training ◽  
The Neural Network

Abstract The article examines the identification of human bone fractures using convoluted neural networks. The method of recognition of photographs of patients is intended for automated systems of identification and video recording of images. Convolutional neural networks have a number of advantages, such as invariability when reducing or increasing image size, immunity to photo movements and deviations, changes in image perspective, and many other image errors. In addition, convolutional neural networks allow you to combine neurons at a local level in two dimensions, connect photographic elements in any place, and also reduce the total number of weights. The work describes a multi-layer convolutional network. The layers of which it consists are divided into two types: convolutional and sub-selective. Of interest is the use of the principle of weighting in the work. This principle allows you to reduce the number of characteristics of the neural network that can be trained. Network training is based on the rule of minimizing empirical error. This rule is based on the algorithm of inverse error propagation. This algorithm provides an instant calculation of the gradient of a complex function of several variables in case the function itself is predefined. Neural network training is based on probabilistic method. This method leads to more optimal results due to interference in the restructuring of network weights. The work confirms the axiomatics of the applied neural network, its architecture and its learning algorithm.

scholarly journals Neural Network Based Identification of Material Model Parameters to Capture Experimental Load-deflection Curve

10.14311/636 ◽  
2004 ◽  
Vol 44 (5-6) ◽  
Author(s):  
D. Novák ◽  
D. Lehký
Keyword(s):  
Neural Network ◽  
High Performance ◽  
High Performance Concrete ◽  
Material Model ◽  
Simulation Method ◽  
Model Parameters ◽  
New Approach ◽  
The Neural Network ◽  
Load Deflection Curve

A new approach is presented for identifying material model parameters. The approach is based on coupling stochastic nonlinear analysis and an artificial neural network. The model parameters play the role of random variables. The Monte Carlo type simulation method is used for training the neural network. The feasibility of the presented approach is demonstrated using examples of high performance concrete for prestressed railway sleepers and an example of a shear wall failure. 

Modeling of Magnetorheological Damper Using Back Propagation Neural Network

2014 ◽  
Vol 896 ◽  
pp. 396-400 ◽  
Author(s):  
Ubaidillah ◽  
Gigih Priyandoko ◽  
Muhammad Nizam ◽  
Iwan Yahya
Keyword(s):  
Neural Network ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Magnetorheological Damper ◽  
Model Parameters ◽  
Test Machine ◽  
The Neural Network ◽  
Time Histories ◽  
Magneto Rheological

This paper presents a new approach to model magneto-rheological (MR) dampers for semi-active suspension systems. The neural network method using adaptive back-propagation learning algorithm real is proposed. The experimental data collected from suspension test machine consist in time histories of current, displacement, velocity and force measured both for constant and variable current. The model parameters are determined using a set of experimental measurements corresponding to different current constant values. It has been shown that the damper response can be satisfactorily predicted with this model.

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