Genetic Algorithm Neural Network(GANN)for multi-class was used to predict the ripeness grades of oil palm fresh fruit using Near Infrared(NIR)spectral data.NIR spectral data provide sufficient information about compou...Genetic Algorithm Neural Network(GANN)for multi-class was used to predict the ripeness grades of oil palm fresh fruit using Near Infrared(NIR)spectral data.NIR spectral data provide sufficient information about compound structure of samples from the near infrared light that passes through.The variables used in the GANN modeling process were the new variables obtained as a result of dimensional reduction from original NIR spectral data using Principal Component Analysis(PCA).Three statistical measures such asMean Absolute Error(MAE),Root Mean Squared Error(RMSE)and the percentage(%)of good classification were used to assess adequacy of the GANN model.Based on the results,the GANN model created was precise enough to be used as the model calibration for this multi-class problem.展开更多
文摘Genetic Algorithm Neural Network(GANN)for multi-class was used to predict the ripeness grades of oil palm fresh fruit using Near Infrared(NIR)spectral data.NIR spectral data provide sufficient information about compound structure of samples from the near infrared light that passes through.The variables used in the GANN modeling process were the new variables obtained as a result of dimensional reduction from original NIR spectral data using Principal Component Analysis(PCA).Three statistical measures such asMean Absolute Error(MAE),Root Mean Squared Error(RMSE)and the percentage(%)of good classification were used to assess adequacy of the GANN model.Based on the results,the GANN model created was precise enough to be used as the model calibration for this multi-class problem.
