A Fig-Based Method for Prediction Alumina Concentration

2012 ◽  
Vol 4 (4) ◽  
pp. 41-50 ◽  
Author(s):  
Jun Yi ◽  
Jun Peng ◽  
Taifu Li
Keyword(s):  
Time Series Data ◽  
Prediction Method ◽  
Information Support ◽  
Series Data ◽  
Support Vector ◽  
Redundant Information ◽  
Alumina Concentration ◽  
Fuzzy Information ◽  
Information Granulation ◽  
Fuzzy Information Granulation

Existing prediction model can not be established accurately as a result of there is often a lot of redundant information in observed values of alumina concentration. A prediction method based on fuzzy information granulation for alumina concentration is proposed to solve above problem. In the proposed approach, theory of fuzzy information granulation was used to granulate time-series data of alumina cell. Granulated data can not only reflect the characteristics of original but also reduce redundant information. Support vector machine was employed as predictor. The experimental results using real data of 170KA operating aluminum cell from a factory demonstrate the efficiency of the designed method and the viability of the technique.

scholarly journals Prediction of heating load fluctuation based on fuzzy information granulation and support vector machine

2020 ◽  
pp. 307-307
Author(s):  
Tao Wang ◽  
Tingyu Ma ◽  
Dongsong Yan ◽  
Jing Song ◽  
Jianshuo Hu ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Prediction Model ◽  
District Heating ◽  
Series Data ◽  
Support Vector ◽  
Fuzzy Information ◽  
District Heating System ◽  
Information Granulation ◽  
Heating Load ◽  
Fuzzy Information Granulation

District heating systems are an important part of the future smart energy system and are seen as a tool to achieve energy efficiency goals in the EU. In order to achieve the real sense of heating on demand, based on historical heating load data, first of all, the heating load time series data was dealing with fuzzy information granulation, and then the cross-validation was used to explore the advantages of the data potential. Then the support vector machine regression prediction model was used for the prediction of the granulation data, finally, the heating load of a district heating system is simulated and verified. The simulation results show that the prediction model can effectively predict the trend of heating load, and provide a theoretical basis for the prediction of district heating load.

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.

scholarly journals A Novel Agricultural Commodity Price Forecasting Model Based on Fuzzy Information Granulation and MEA-SVM Model

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Yongli Zhang ◽  
Sanggyun Na
Keyword(s):  
Price Index ◽  
Commodity Price ◽  
Series Data ◽  
Forecasting Model ◽  
Price Forecasting ◽  
Fuzzy Information ◽  
Agricultural Commodity ◽  
Information Granulation ◽  
Svm Model ◽  
Fuzzy Information Granulation

Accurately predicting the price of agricultural commodity is very important for evading market risk, increasing agricultural income, and accomplishing government macroeconomic regulation. With the price index predictions of 6 commodities of Food and Agriculture Organization of the United Nations (FAO) as examples, this paper proposed a novel agricultural commodity price forecasting model which combined the fuzzy information granulation, mind evolutionary algorithm (MEA), and support vector machine (SVM). Firstly, the time series data of agricultural commodity price index was transformed into fuzzy information granulation particles made up ofLow,R, andUp, which represented the trend and magnitude of price movement. Secondly, MEA algorithm was employed to seek the optimal parameterscandgfor SVM to establish the MEA-SVM model. Finally, FOA price index fluctuation range and change trend in the future were predicted by the MEA-SVM model. The empirical analysis showed that the MEA-SVM model was effective and had higher prediction accuracy and faster calculation speed in the forecasting of agricultural commodity price.

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.

Sign in / Sign up

Export Citation Format

Share Document