A deep learned type-2 fuzzy neural network: Singular value decomposition approach

2021 ◽  
pp. 107244
Author(s):  
Sultan Noman Qasem ◽  
Ardashir Mohammadzadeh
Keyword(s):  
Neural Network ◽  
Singular Value Decomposition ◽  
Fuzzy Neural Network ◽  
Singular Value ◽  
Decomposition Approach ◽  
Fuzzy Neural ◽  
Value Decomposition

scholarly journals Research on Fault Diagnosis Based on Singular Value Decomposition and Fuzzy Neural Network

2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Jingbo Gai ◽  
Yifan Hu
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Singular Value Decomposition ◽  
Fuzzy Neural Network ◽  
Singular Value ◽  
Characteristic Matrix ◽  
Gradual Change ◽  
Fault Features ◽  
Fuzzy Neural ◽  
Value Decomposition

A method based on singular value decomposition (SVD) and fuzzy neural network (FNN) was proposed to extract and diagnose the fault features of diesel engine crankshaft bearings efficiently and accurately. Firstly, vibration signals of crankshaft bearings in known state under the same working condition were decomposed by EMD to obtain the modal components containing fault-feature information. Then, the singular values of modal components which include the main fault features were used as the initial vector matrix, where the eigenvectors were decomposed to form a fault characteristic matrix. At last, the fault features matrix was trained by the fuzzy neural network, in order to realize the diagnosis and identification of the crankshaft bearings in different states in the form of numerical values. The experiment showed that the numerical identification of the fuzzy neural network based on the singular value had high fault diagnosis accuracy and stability. This method can also reflect the gradual change of the crankshaft bearings’ fault to some extent, so it has the desired reliability and value.

Inverse Control of Cable-driven Parallel Mechanism Using Type-2 Fuzzy Neural Network

2010 ◽  
Vol 36 (3) ◽  
pp. 459-464 ◽  
Author(s):  
Cheng-Dong LI ◽  
Jian-Qiang YI ◽  
Yi YU ◽  
Dong-Bin ZHAO
Keyword(s):  
Neural Network ◽  
Parallel Mechanism ◽  
Fuzzy Neural Network ◽  
Inverse Control ◽  
Fuzzy Neural

scholarly journals A singular value decomposition approach for improved taxonomic classification of biological sequences

BMC Genomics ◽  
2011 ◽  
Vol 12 (Suppl 4) ◽  
pp. S11 ◽  
Author(s):  
Anderson R Santos ◽  
Marcos A Santos ◽  
Jan Baumbach ◽  
John A McCulloch ◽  
Guilherme C Oliveira ◽  
...  
Keyword(s):  
Singular Value Decomposition ◽  
Singular Value ◽  
Taxonomic Classification ◽  
Biological Sequences ◽  
Decomposition Approach ◽  
Value Decomposition

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%.

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