EEG classification using recurrent adaptive neuro-fuzzy network based on time-series prediction

2017 ◽  
Vol 31 (7) ◽  
pp. 2551-2562 ◽  
Author(s):  
Hossein Komijani ◽  
Mohammad Reza Parsaei ◽  
Ebrahim Khajeh ◽  
Mohammad Javad Golkar ◽  
Houman Zarrabi
Keyword(s):  
Time Series ◽  
Time Series Prediction ◽  
Eeg Classification ◽  
Neuro Fuzzy ◽  
Fuzzy Network

ANNEALED CHAOTIC LEARNING FOR TIME SERIES PREDICTION IN IMPROVED NEURO-FUZZY NETWORK WITH FEEDBACKS

2009 ◽  
Vol 08 (04) ◽  
pp. 429-444 ◽  
Author(s):  
CATHERINE VAIRAPPAN ◽  
SHANGCE GAO ◽  
ZHENG TANG ◽  
HIROKI TAMURA
Keyword(s):  
Time Series ◽  
Fuzzy System ◽  
Chaotic Dynamics ◽  
Time Series Prediction ◽  
Data Series ◽  
Temporal Data ◽  
Optimal Solutions ◽  
Benchmark Data ◽  
Neuro Fuzzy ◽  
Fuzzy Network

A new version of neuro-fuzzy system of feedbacks with chaotic dynamics is proposed in this work. Unlike the conventional neuro-fuzzy, improved neuro-fuzzy system with feedbacks is better able to handle temporal data series. By introducing chaotic dynamics into the feedback neuro-fuzzy system, the system has richer and more flexible dynamics to search for near-optimal solutions. In the experimental results, performance and effectiveness of the presented approach are evaluated by using benchmark data series. Comparison with other existing methods shows the proposed method for the neuro-fuzzy feedback is able to predict the time series accurately.

Intelligent forecasting of time series based on evolving distributed Neuro‐Fuzzy network

2020 ◽  
Vol 36 (3) ◽  
pp. 1394-1413
Author(s):  
Selmo Eduardo Rodrigues Júnior ◽  
Ginalber Luiz Oliveira Serra
Keyword(s):  
Time Series ◽  
Neuro Fuzzy ◽  
Fuzzy Network

scholarly journals Flexible Neo-fuzzy Neuron and Neuro-fuzzy Network for Monitoring Time Series Properties

2013 ◽  
Vol 16 (1) ◽  
Author(s):  
Yevgeniy Bodyanskiy ◽  
Iryna Pliss ◽  
Olena Vynokurova
Keyword(s):  
Time Series ◽  
Monitoring Time ◽  
Neuro Fuzzy ◽  
Fuzzy Network

Fast evolving neuro-fuzzy model and its application in online classification and time series prediction

2011 ◽  
Vol 15 (3) ◽  
pp. 279-288 ◽  
Author(s):  
Hossein Soleimani-B. ◽  
Caro Lucas ◽  
Babak N. Araabi
Keyword(s):  
Time Series ◽  
Time Series Prediction ◽  
Fuzzy Model ◽  
Online Classification ◽  
Neuro Fuzzy

scholarly journals A Distributed Adaptive Neuro-Fuzzy Network for Chaotic Time Series Prediction

2015 ◽  
Vol 15 (1) ◽  
pp. 24-33 ◽  
Author(s):  
Margarita Terziyska
Keyword(s):  
Time Series Prediction ◽  
Model Performance ◽  
Inference Mechanism ◽  
Neuro Fuzzy ◽  
Learning Procedure ◽  
Gradient Descent Algorithm ◽  
Chaotic Time Series Prediction ◽  
Takagi Sugeno ◽  
Fuzzy Network ◽  
Time Systems

Abstract In this paper a Distributed Adaptive Neuro-Fuzzy Architecture (DANFA) model with a second order Takagi-Sugeno inference mechanism is presented. The proposed approach is based on the simple idea to reduce the number of the fuzzy rules and the computational load, when modeling nonlinear systems. As a learning procedure for the designed structure a two-step gradient descent algorithm with a fixed learning rate is used. To demonstrate the potentials of the selected approach, simulation experiments with two benchmark chaotic time systems − Mackey-Glass and Rossler are studied. The results obtained show an accurate model performance with a minimal prediction error.

Batch type local search-based adaptive neuro-fuzzy inference system (ANFIS) with self-feedbacks for time-series prediction

2009 ◽  
Vol 72 (7-9) ◽  
pp. 1870-1877 ◽  
Author(s):  
Catherine Vairappan ◽  
Hiroki Tamura ◽  
Shangce Gao ◽  
Zheng Tang
Keyword(s):  
Time Series ◽  
Local Search ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Time Series Prediction ◽  
Inference System ◽  
Batch Type ◽  
Neuro Fuzzy
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