INVESTIGATION OF A FUZZY-NEURAL NETWORK APPLICATION IN CLASSIFICATION OF SOILS USING GROUND-PENETRATING RADAR IMAGERY

2004 ◽  
Vol 20 (1) ◽  
pp. 109-117 ◽  
Author(s):  
L. O. Odhiambo ◽  
R. S. Freeland ◽  
R. E. Yoder ◽  
J. W. Hines
Keyword(s):  
Neural Network ◽  
Ground Penetrating Radar ◽  
Fuzzy Neural Network ◽  
Fuzzy Neural ◽  
Radar Imagery ◽  
Network Application ◽  
Ground Penetrating

scholarly journals Klasifikasi Motif Batik Berbasis Kemiripan Ciri dengan Wavelet Transform dan Fuzzy Neural Network

2014 ◽  
Vol 5 (1) ◽  
pp. 361
Author(s):  
A Haris Rangkuti
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Fuzzy Neural Network ◽  
Classification Methods ◽  
Neural Network Classification ◽  
Fuzzy Input ◽  
Fuzzy Neural ◽  
Input Variables

 This paper introduces a classification of the image of the batik process, which is based on the similarity of the characteristics, by combining the method of wavelet transform Daubechies type 2 level 2, to process the characteristic texture consisting of standard deviation, mean and energy as input variables, using the method of Fuzzy Neural Network (FNN). Fuzzyfikasi process will be carried out all input values with five categories: Very Low (VL), Low (L), Medium (M), High (H) and Very High (VH). The result will be a fuzzy input in the process of neural network classification methods. The result will be a fuzzy input in the process of neural network classification methods. For the image to be processed seven types of batik motif is ceplok, kawung, lereng, parang, megamendung, tambal and nitik. The results of the classification process with FNN is rule generation, so for the new image of batik can be immediately known motif types after treatment with FNN classification.  For the degree of precision of this method is 86-92%.

scholarly journals Neural Networks as a Tool for Georadar Data Processing

2015 ◽  
Vol 25 (4) ◽  
pp. 955-960 ◽  
Author(s):  
Piotr Szymczyk ◽  
Sylwia Tomecka-Suchoń ◽  
Magdalena Szymczyk
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Data Processing ◽  
Ground Penetrating Radar ◽  
Feature Vector ◽  
Special Kind ◽  
Automatic Classification ◽  
Geologic Structure ◽  
Ground Penetrating

Abstract In this article a new neural network based method for automatic classification of ground penetrating radar (GPR) traces is proposed. The presented approach is based on a new representation of GPR signals by polynomials approximation. The coefficients of the polynomial (the feature vector) are neural network inputs for automatic classification of a special kind of geologic structure—a sinkhole. The analysis and results show that the classifier can effectively distinguish sinkholes from other geologic structures.

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