Fruit production forecasting by neuro-fuzzy techniques
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Neuro-fuzzy techniques are finding a practical application
in many fields such as in model identification and forecasting of linear
and non-linear systems. This paper presents a neuro-fuzzy model for
forecasting the fruit production of some agriculture products (olives,
lemons, oranges, cherries and pistachios). The model utilizes a time
series of yearly data. The fruit forecasting is based on Adaptive
Neural Fuzzy Inference System (ANFIS). ANFIS uses a combination
of the least-squares method and the backprobagation gradient
descent method to estimate the optimal food forecast parameters for
each year. The results are compared to those of an Autoregressive
(AR) model and an Autoregressive Moving Average model (ARMA).