Hydrological modeling using a dynamic neuro-fuzzy system with on-line and local learning algorithm

2009 ◽  
Vol 32 (1) ◽  
pp. 110-119 ◽  
Author(s):  
Yoon-Seok Timothy Hong ◽  
Paul A. White
Keyword(s):  
Fuzzy System ◽  
Hydrological Modeling ◽  
Learning Algorithm ◽  
Local Learning ◽  
Neuro Fuzzy ◽  
On Line

scholarly journals Experimental study of a novel neuro-fuzzy system for on-line handwritten UNIPEN digit recognition

1998 ◽  
Vol 19 (3-4) ◽  
pp. 357-364 ◽  
Author(s):  
E. Gómez Sánchez ◽  
J.A. Gago González ◽  
Y.A. Dimitriadis ◽  
J.M. Cano Izquierdo ◽  
J. López Coronado
Keyword(s):  
Experimental Study ◽  
Fuzzy System ◽  
Digit Recognition ◽  
Neuro Fuzzy ◽  
On Line

Deep Stacking Convex Neuro-Fuzzy System and Its On-line Learning

2017 ◽  
pp. 49-59 ◽  
Author(s):  
Yevgeniy Bodyanskiy ◽  
Olena Vynokurova ◽  
Iryna Pliss ◽  
Dmytro Peleshko ◽  
Yuriy Rashkevych
Keyword(s):  
Fuzzy System ◽  
Neuro Fuzzy ◽  
On Line ◽  
On Line Learning

scholarly journals ON-LINE NEO-PHASE AUTOENKODER FOR SYSTEMS WITH DEEP LEARNING ON THE BASE OF THE KOLMOGOROV’S NEURO-PHASE NETWORK

2017 ◽  
pp. 60-67
Author(s):  
Є.В. БОДЯНСЬКИЙ ◽  
О.А. ВИНОКУРОВА ◽  
Д.Д. ПЕЛЕШКО ◽  
Ю.М. РАШКЕВИЧ
Keyword(s):  
Neural Networks ◽  
Fuzzy System ◽  
Deep Neural Networks ◽  
Significant Loss ◽  
High Dimensional ◽  
Batch Mode ◽  
Neuro Fuzzy ◽  
Main Disadvantage ◽  
On Line ◽  
Inherent Part

One of the important problem, which is connected with big high dimensional data processing, is the task of their compression without significant loss of information that is contained in this data. The systems, which solve this problem and are called autoencoders, are the inherent part of deep neural networks. The main disadvantage of well-known autoencoders is low speed of learning process, which is implemented in the batch mode. In the paper the two-layered autoencoder is proposed. This system is the modification of Kolmogorov’s neuro-fuzzy system. Thus, in the paper the hybrid neo-fuzzy syste-  mencoder is proposed that has essentially advantages comparatively with conventional neurocompressors-encoders.

scholarly journals A neuro-fuzzy system to support in the diagnostic of epileptic events and non-epileptic events using different fuzzy arithmetical operations

2008 ◽  
Vol 66 (2a) ◽  
pp. 179-183 ◽  
Author(s):  
Lucimar M.F. de Carvalho ◽  
Silvia Modesto Nassar ◽  
Fernando Mendes de Azevedo ◽  
Hugo José Teixeira de Carvalho ◽  
Lucas Lese Monteiro ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fuzzy System ◽  
Learning Algorithm ◽  
Test Phase ◽  
Qualitative Approach ◽  
Fuzzy Classification ◽  
Neuro Fuzzy ◽  
The Feminine ◽  
Artificial Neural

OBJECTIVE: To investigate different fuzzy arithmetical operations to support in the diagnostic of epileptic events and non epileptic events. METHOD: A neuro-fuzzy system was developed using the NEFCLASS (NEuro Fuzzy CLASSIfication) architecture and an artificial neural network with backpropagation learning algorithm (ANNB). RESULTS: The study was composed by 244 patients with a bigger frequency of the feminine sex. The number of right decisions at the test phase, obtained by the NEFCLASS and ANNB was 83.60% and 90.16%, respectively. The best sensibility result was attained by NEFCLASS (84.90%); the best specificity result were attained by ANNB with 95.65%. CONCLUSION: The proposed neuro-fuzzy system combined the artificial neural network capabilities in the pattern classifications together with the fuzzy logic qualitative approach, leading to a bigger rate of system success.

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