Application of Fuzzy-Neural Network in Classification of Soils using Ground-penetrating Radar Imagery

2002 ◽  
Author(s):  
Lameck O. Odhiambo ◽  
Robert S. Freeland ◽  
Ronald E. Yoder ◽  
J. Wesley Hines
Keyword(s):  
Neural Network ◽  
Ground Penetrating Radar ◽  
Fuzzy Neural Network ◽  
Fuzzy Neural ◽  
Radar Imagery ◽  
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.

Fuzzy Classification of Gear Fault Using Principal Component Analysis-Based Fuzzy Neural Network

2020 ◽  
Author(s):  
Kai Zhou ◽  
J. Tang
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Fuzzy Neural Network ◽  
Principal Component ◽  
Component Analysis ◽  
Fuzzy Classification ◽  
Vibration Data ◽  
Gear Fault ◽  
Fuzzy Neural

Abstract Condition assessment of machinery components such as gears is important to maintain their normal operations and thus can bring benefit to their life circle management. Data-driven approaches haven been a promising way for such gear condition monitoring and fault diagnosis. In practical situation, gears generally have a variety of fault types, some of which exhibit continuous severities of fault. Vibration data collected oftentimes are limited to reflect all possible fault types. Therefore, there is practical need to utilize the data with a few discrete fault severities in training and then infer fault severities for the general scenario. To achieve this, we develop a fuzzy neural network (FNN) model to classify the continuous severities of gear faults based on the experimental measurement. Principal component analysis (PCA) is integrated with the FNN model to capture the main features of the time-series vibration signals with dimensional reduction for the sake of computational efficiency. Systematic case studies are carried out to validate the effectiveness of proposed methodology.

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