Interpretability and mean-square error performance of fuzzy inference systems For Data Mining

2005 ◽  
Vol 13 (4) ◽  
pp. 185-196 ◽  
Author(s):  
Ashwani Kumar
Keyword(s):  
Data Mining ◽  
Mean Square Error ◽  
Fuzzy Inference ◽  
Mean Square ◽  
Fuzzy Inference Systems ◽  
Error Performance ◽  
Inference Systems ◽  
Mean Square Error Performance

scholarly journals Building Java Intelligent Applications Data Mining for Java Type-2 Fuzzy Inference Systems

2015 ◽  
Vol 51 ◽  
pp. 2719-2728 ◽  
Author(s):  
Manuel Castañón-Puga ◽  
Josué Miguel Flores-Parra ◽  
Juan Ramón Castro ◽  
Carelia Gaxiola-Pacheco ◽  
Luis Enrique Palafox-Maestre
Keyword(s):  
Data Mining ◽  
Fuzzy Inference ◽  
Fuzzy Inference Systems ◽  
Inference Systems

scholarly journals Comparison of Power Output Forecasting on the Photovoltaic System Using Adaptive Neuro-Fuzzy Inference Systems and Particle Swarm Optimization-Artificial Neural Network Model

Energies ◽  
2020 ◽  
Vol 13 (2) ◽  
pp. 351 ◽  
Author(s):  
Promphak Dawan ◽  
Kobsak Sriprapha ◽  
Songkiate Kittisontirak ◽  
Terapong Boonraksa ◽  
Nitikorn Junhuathon ◽  
...  
Keyword(s):  
Neural Network ◽  
Power Output ◽  
Fuzzy Inference ◽  
Photovoltaic System ◽  
Mean Square ◽  
Fuzzy Inference Systems ◽  
Swarm Optimization ◽  
Pv Systems ◽  
Neuro Fuzzy ◽  
Inference Systems

The power output forecasting of the photovoltaic (PV) system is essential before deciding to install a photovoltaic system in Nakhon Ratchasima, Thailand, due to the uneven power production and unstable data. This research simulates the power output forecasting of PV systems by using adaptive neuro-fuzzy inference systems (ANFIS), comparing accuracy with particle swarm optimization combined with artificial neural network methods (PSO-ANN). The simulation results show that the forecasting with the ANFIS method is more accurate than the PSO-ANN method. The performance of the ANFIS and PSO-ANN models were verified with mean square error (MSE), root mean square error (RMSE), mean absolute error (MAP) and mean absolute percent error (MAPE). The accuracy of the ANFIS model is 99.8532%, and the PSO-ANN method is 98.9157%. The power output forecast results of the model were evaluated and show that the proposed ANFIS forecasting method is more beneficial compared to the existing method for the computation of power output and investment decision making. Therefore, the analysis of the production of power output from PV systems is essential to be used for the most benefit and analysis of the investment cost.

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