关键词: 人工智能算法, 土壤墒情预测, 自适应, 遗传多样性函数, 神经网络

Abstract: Forecasting soil moisture accurately is very important to monitor plant growing. Researchers are resorting to hybrid intelligence algorithms fusing more effective strategies into prediction process. Combination optimization can overcome the disadvantages of single method and improve predictive quality. This paper advances a novel algorithm to conquer the prematurity and sawtooth of traditional neural network. Firstly, it proposes the conception of genetic diversity function which measures genetic diversity of population. Secondly, it uses adaptive crossover strategy and mutation strategy to obtain the best initial weights and thresholds. Finally, it receives neural network results with better precision and efficiency and less iterations. Simulations reveal that in contrast to other genetic neural network, the quality of the soil moisture forecast has a great improvement in the new algorithm.

Key words: artificial intelligence algorithm, soil moisture prediction, adaptive, genetic diversity function, neural network