Risk analysis for agricultural drought based on neural network optimized by chaos algorithm

Based on the idea of fitting function curve with neural network, this paper establishes the neural network models for estimating agricultural drought quantitatively and describing its probability distribution. The models based on this idea can avoid the inconvenience of establishing concrete mathematical formulas and the calculation of parameters, and this method can fit the probability function when the theoretic distribution of the random variable is unknown. On the choice of training method of network, gradient descending method is combined with chaos algorithm, which can make network find the global minimum quickly. Finally this paper calculates probability distribution of the drought extent of agriculture in Qucun irrigation area, Puyang city, Henan province, validating the correctness of the models.