Identification of cumulative fruit responses during storage using neural networks (Identifikasi Terhadap Respon Kumulatif Buah Selma Penyimpanan Dengan Metode Neural Netvork)

Abstract

Neural networks are useful to identify complex nonlinear relationships between input and output of a system. Cumulative fruit responses such as water losses and ripening during storage are characterized non-linearly. For identification, several patterns of these cumulative responses, as affected by environmental factors, are often conducted by repealing the experiment several times under different environmental conditions. It is not well-known how many response patterns (training data sets) are necessary for an acceptable identification. This research explores an effective way to identify the cumulative responses of tomato during storage using neural networks. Firstly, data for identification were obtained from a mathematical model. Secondly, the relationship between the number of response pattern and the estimation error were investigated. The estimated error becomes smaller when the number of response pattern is three or more. This suggests that three types of response patterns allow cumulative responses to be successfully identified. Besides, an addition of linear data (1, 2, .., N) as input variable significantly improves the identification accuracy of the cumulative response. Finally, the identification of actual data was implemented based on these results and the satisfactory results will be obtained. Keywords: Storage process, dynamic system, cumulative fruit responses, identification, neural network