scholarly journals k-NN Based Neuro-fuzzy System for Time Series Prediction

2013 ◽  
Author(s):  
Chia-Ching Wei ◽  
Thao-Tsen Chen ◽  
Shie-Jue Lee
Keyword(s):  
Time Series ◽  
Fuzzy System ◽  
Time Series Prediction ◽  
Neuro Fuzzy

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.

scholarly journals Towards a Neuro-Fuzzy System for Time Series Forecasting in Maintenance Applications

2008 ◽  
Vol 41 (2) ◽  
pp. 12855-12860 ◽  
Author(s):  
M. El-Koujok ◽  
R. Gouriveau ◽  
N. Zerhouni
Keyword(s):  
Time Series ◽  
Fuzzy System ◽  
Time Series Forecasting ◽  
Neuro Fuzzy

scholarly journals Electrical Insulator Fault Forecasting Based on a Wavelet Neuro-Fuzzy System

Energies ◽  
2020 ◽  
Vol 13 (2) ◽  
pp. 484 ◽  
Author(s):  
Stéfano Frizzo Stefenon ◽  
Roberto Zanetti Freire ◽  
Leandro dos Santos Coelho ◽  
Luiz Henrique Meyer ◽  
Rafael Bartnik Grebogi ◽  
...  
Keyword(s):  
Time Series ◽  
Fuzzy System ◽  
Time Series Forecasting ◽  
Subtractive Clustering ◽  
Anfis Model ◽  
Data Set ◽  
Inference System ◽  
Electrical Insulator ◽  
Fuzzy C Means ◽  
Neuro Fuzzy

The surface contamination of electrical insulators can increase the electrical conductivity of these components, which may lead to faults in the electrical power system. During inspections, ultrasound equipment is employed to detect defective insulators or those that may cause failures within a certain period. Assuming that the signal collected by the ultrasound device can be processed and used for both the detection of defective insulators and prediction of failures, this study starts by presenting an experimental procedure considering a contaminated insulator removed from the distribution line for data acquisition. Based on the obtained data set, an offline time series forecasting approach with an Adaptive Neuro-Fuzzy Inference System (ANFIS) was conducted. To improve the time series forecasting performance and to reduce the noise, Wavelet Packets Transform (WPT) was associated to the ANFIS model. Once the ANFIS model associated with WPT has distinct parameters to be adjusted, a complete evaluation concerning different model configurations was conducted. In this case, three inference system structures were evaluated: grid partition, fuzzy c-means clustering, and subtractive clustering. A performance analysis focusing on computational effort and the coefficient of determination provided additional parameter configurations for the model. Taking into account both parametrical and statistical analysis, the Wavelet Neuro-Fuzzy System with fuzzy c-means showed that it is possible to achieve impressive accuracy, even when compared to classical approaches, in the prediction of electrical insulators conditions.

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