Prediction and optimal allocation of agricultural non-point source pollution based on chaos theory Online publication date: Tue, 27-Feb-2018
by Li Chenyang; Cheng Na; Sun Nan
International Journal of Reasoning-based Intelligent Systems (IJRIS), Vol. 9, No. 3/4, 2017
Abstract: In order to promote the accuracy of agricultural pollution source prediction algorithm, an agricultural pollution source prediction algorithm based on chaotic differential evolution algorithm neural network is proposed in the article. Firstly, it initialises the population of differential evolution algorithm with chaos theory, to promote the diversity for initial population solution; then it improves the differential evolution algorithm by using mean entropy and perturbation variation method to promote its optimise performance. Secondly, it optimises the neural network parameter learning process by using the improved differential evolution algorithm to promote the accuracy of parameters optimisation. Lastly, it applies the proposed algorithm into the example of local agricultural pollution source prediction and the results showed that the proposed method could effectively increase the accuracy of agricultural pollution source prediction.
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