Sunspot interval prediction based on fuzzy information granulation and extreme learning machine

2020 ◽  
Vol 41 (1) ◽  
Author(s):  
Peng Lingling
Keyword(s):  
Extreme Learning Machine ◽  
Fuzzy Information ◽  
Information Granulation ◽  
Interval Prediction ◽  
Learning Machine ◽  
Fuzzy Information Granulation

scholarly journals An Efficient Extreme Learning Machine Based on Fuzzy Information Granulation

2017 ◽  
Vol 12 (06) ◽  
pp. 161
Author(s):  
Yuehua Gao ◽  
Tianyi Chen
Keyword(s):  
Extreme Learning Machine ◽  
Classification Problem ◽  
Fuzzy Information ◽  
New Approach ◽  
Generalization Ability ◽  
Information Granulation ◽  
Learning Efficiency ◽  
Learning Machine ◽  
Hidden Nodes ◽  
Fuzzy Information Granulation

In order to improve learning efficiency and generalization ability of extreme learning machine (ELM), an efficient extreme learning machine based on fuzzy information granulation (FIG) is put forward. The new approach not only improves the speed of basic ELM algorithm that contains many hidden nodes, but also overcomes the weakness of basic ELM of low learning efficiency and generalization ability by getting rid of redundant information in the observed values. The experimental results show that the proposed method is effective and can produce desirable generalization performance in most cases based on a few regression and classification problem.

scholarly journals An Efficient Extreme Learning Machine Based on Fuzzy Information Granulation

2015 ◽  
Vol 11 (8) ◽  
pp. 42
Author(s):  
Xia-fu LV ◽  
Jun-peng CHEN ◽  
Lei LIU ◽  
Bo-hua WANG ◽  
Yong WANG
Keyword(s):  
Extreme Learning Machine ◽  
Original Data ◽  
Redundant Information ◽  
Fuzzy Information ◽  
Data Set ◽  
Generalization Ability ◽  
Information Granulation ◽  
Learning Efficiency ◽  
Learning Machine ◽  
Fuzzy Information Granulation

In order to improve learning efficiency and generalization ability of extreme learning machine (ELM), an efficient extreme learning machine based on fuzzy information granulation (FIG) is put forward. Firstly, using FIG to get rid of redundant information in the original data set and then ELM is used to do train granulated data for prediction. This method not only improves the speed of basic ELM algorithm that contains many hidden nodes, but also overcomes the weakness of basic ELM of low learning efficiency and generalization ability by getting rid of redundant information in the observed values. The experimental results show that the proposed method is effective and can produce desirable generalization performance in most cases based on a few regression and classification problem.

A data-driven fuzzy information granulation approach for battery state of health forecasting

2020 ◽  
Vol 475 ◽  
pp. 228716 ◽  
Author(s):  
Wenjie Pan ◽  
Qi Chen ◽  
Maotao Zhu ◽  
Jie Tang ◽  
Jianling Wang
Keyword(s):  
Data Driven ◽  
State Of Health ◽  
Fuzzy Information ◽  
Information Granulation ◽  
Fuzzy Information Granulation

Short-Term Wind Speed and Power Prediction Using Fuzzy Information Granulation-Support Vector Machine

2012 ◽  
Vol 608-609 ◽  
pp. 814-817
Author(s):  
Xiao Fu ◽  
Dong Xiang Jiang
Keyword(s):  
Support Vector Machine ◽  
Wind Speed ◽  
Wind Farm ◽  
Support Vector ◽  
Fuzzy Information ◽  
Short Term ◽  
Power Fluctuation ◽  
Data Set ◽  
Information Granulation ◽  
Fuzzy Information Granulation

The power fluctuation of wind turbine often causes serious problems in electricity grids. Therefore, short term prediction of wind speed and power as to eliminate the uncertainty determined crucially the development of wind energy. Compared with physical methods, support vector machine (SVM) as an intelligent artificial method is more general and shows better nonlinear modeling capacity. A model which combined fuzzy information granulation with SVM method was developed and implemented in short term future trend prediction of wind speed and power. The data, including the daily wind speed and power, from a wind farm in northern China were used to evaluate the proposed method. The prediction results show that the proposed model performs better and more stable than the standard SVM model when apply them into the same data set.

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