Journal ArticleDOI
Neural network modeling and prediction of resistivity structures using VES Schlumberger data over a geothermal area
Reads0
Chats0
TLDR
ANN based methods are significantly faster and efficient for detection of complex layered resistivity structures with a relatively greater degree of precision and resolution.About:
This article is published in Computers & Geosciences.The article was published on 2013-03-01. It has received 31 citations till now.read more
Citations
More filters
Journal ArticleDOI
Uncertainty and Resolution Analysis of 2D and 3D Inversion Models Computed from Geophysical Electromagnetic Data
Zhengyong Ren,Thomas Kalscheuer +1 more
TL;DR: This review tries to cover linearised model analysis tools such as the sensitivity matrix, the model resolution matrix and the model covariance matrix also providing a partially nonlinear description of the equivalent model domain based on pseudo-hyperellipsoids and emphasises linearisedmodel analysis, as efficient computation of nonlinear model uncertainty and resolution estimates is mainly determined by fast forward and inversion solvers.
Journal ArticleDOI
Efficient Monte Carlo sampling of inverse problems using a neural network-based forward—applied to GPR crosshole traveltime inversion
Journal ArticleDOI
Experimental-artificial intelligence approach for characterizing electrical resistivity of partially saturated clay liners
TL;DR: In this paper, the authors investigated the evolution of electrical resistivity of different kaolinite-dominant clay liners, in terms of its soil composition, as its moisture content and dry density change.
Journal ArticleDOI
BP neural network and improved differential evolution for transient electromagnetic inversion
TL;DR: A chaotic mutation and crossover with constraint factor DE (CCDE) is proposed in improving the global optimization ability of the differential evolution Algorithm (DE) for amending BP's sensitive to initial parameters.
References
More filters
Book
Neural Networks: A Comprehensive Foundation
TL;DR: Thorough, well-organized, and completely up to date, this book examines all the important aspects of this emerging technology, including the learning process, back-propagation learning, radial-basis function networks, self-organizing systems, modular networks, temporal processing and neurodynamics, and VLSI implementation of neural networks.
Book
Neural networks for pattern recognition
TL;DR: This is the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition, and is designed as a text, with over 100 exercises, to benefit anyone involved in the fields of neural computation and pattern recognition.
Book ChapterDOI
Learning internal representations by error propagation
TL;DR: This chapter contains sections titled: The Problem, The Generalized Delta Rule, Simulation Results, Some Further Generalizations, Conclusion.
Book
Learning internal representations by error propagation
TL;DR: In this paper, the problem of the generalized delta rule is discussed and the Generalized Delta Rule is applied to the simulation results of simulation results in terms of the generalized delta rule.
Book ChapterDOI
Neural Networks for Pattern Recognition
Suresh Kothari,Heekuck Oh +1 more
TL;DR: The chapter discusses two important directions of research to improve learning algorithms: the dynamic node generation, which is used by the cascade correlation algorithm; and designing learning algorithms where the choice of parameters is not an issue.
Related Papers (5)
Simultaneous parameter identification of a heterogeneous aquifer system using artificial neural networks
Halil Karahan,M. Tamer Ayvaz +1 more