A Type 2 Fuzzy Neural Network Ensemble to Estimate Time Increased Probability of Seismic Hazard in North Region of Baja California Peninsula

2014 ◽  
pp. 125-135
Author(s):  
Victor M. Torres ◽  
Oscar Castillo
Keyword(s):  
Neural Network ◽  
Seismic Hazard ◽  
Fuzzy Neural Network ◽  
Baja California ◽  
Baja California Peninsula ◽  
Neural Network Ensemble ◽  
Fuzzy Neural

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 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 Adaptive Type-2 Fuzzy Neural-Network Control for Teleoperation Systems With Delay and Uncertainties

2020 ◽  
Vol 28 (10) ◽  
pp. 2543-2554 ◽  
Author(s):  
Parham Mohsenzadeh Kebria ◽  
Abbas Khosravi ◽  
Saeid Nahavandi ◽  
Dongrui Wu ◽  
Fernando Bello
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Network Control ◽  
Neural Network Control ◽  
Fuzzy Neural ◽  
Teleoperation Systems

scholarly journals Commercial Vehicle-Based Robust Control of Seated Whole-Body Vibration Using Adaptive Indirect Type-2 Fuzzy Neural Network

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 124949-124960 ◽  
Author(s):  
Hamid Taghavifar ◽  
Bin Xu ◽  
Chuan Hu ◽  
Yechen Qin ◽  
Chongfeng Wei
Keyword(s):  
Neural Network ◽  
Robust Control ◽  
Fuzzy Neural Network ◽  
Commercial Vehicle ◽  
Whole Body Vibration ◽  
Whole Body ◽  
Body Vibration ◽  
Fuzzy Neural
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