Classification of multispectral images through a rough-fuzzy neural network

2004 ◽  
Vol 43 (1) ◽  
pp. 103 ◽  
Author(s):  
Shao-Han Liu
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Multispectral Images ◽  
Fuzzy Neural

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%.

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.

MODIFIED FUZZY NEURAL NETWORK FOR THE CLASSIFICATION OF MURDER CASES IN CRIMINAL LAW USING GAUSSIAN MEMBERSHIP FUNCTION

2013 ◽  
Vol 12 (02) ◽  
pp. 1350011 ◽  
Author(s):  
M. M. JANEELA THERESA ◽  
V. JOSEPH RAJ
Keyword(s):  
Neural Network ◽  
Criminal Law ◽  
Network Architecture ◽  
Fuzzy Neural Network ◽  
Classification Model ◽  
Membership Functions ◽  
Real World Data ◽  
Fuzzy Classifier ◽  
Fuzzy Neural

This paper presents the problem of decision making by a judge in the case of murder cases in criminal law using single hidden layered fuzzy neural network algorithm. Since the membership functions (MFs) of fuzzy sets can affect the performance of the classification models, determination of MFs is crucial. In this paper, the MF selected is Triangular and Gaussian is proposed for evaluation to improve the classification results. To evaluate the effectiveness of the proposed Fuzzy Neural Network model for the classification of murder cases, sufficient number of real-world data sets of court decisions are trained and tested. The simulation model of different membership functions for the modified fuzzy neural network architecture is implemented in C++. Experimental results show that the proposed neuro-fuzzy classifier with Gaussian MF outperforms Triangular MF with higher accuracy. The proposed classification model is proved to be a suitable tool for classification of murder cases in criminal law.

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