Abstract
In order to reduce the prediction error of the heavy metal content of farmland soil by General Regression Neural Network (GRNN), an Intelligent Compound Prediction Model (ICPM) was proposed. As the result of Genetic Algorithm optimization is good or bad, it mainly depends on whether it can guarantee the diversity of the population in the optimization process. Based on this, an Improved Genetic Algorithm (IGA) is proposed. IGA introduces the probability adjustment of the sine function transformation and the better gene replacement criterion into the Genetic Algorithm (GA). In the process of IGA’s optimization, the crossover probability and mutation probability continue to increase, ensuring the continuity of the diversity of the population. ICPM is a combined forecasting model of GRNN and IGA. It optimizes the smoothing factor of GRNN through IGA. The process of repeated optimization of IGA is also the process of repeated learning of existing knowledge by GRNN. ICPM not only ensures the continuous optimization of the population, but also the diversity of the population. Combined with the simulation prediction of the content of heavy metals Cr, Cu and Pb in farmland soil in Dongxihu District of Wuhan City, it proved that ICPM has better prediction performance and better generalization performance than GRNN and other models.
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Funding
This work was supported in part by the Major Technical Innovation Projects of Hubei Province under Grant 2018ABA099, in part by the National Science Fund for Youth of Hubei Province of China under Grant 2018CFB408, in part by the Natural Science Foundation of Hubei Province of China under Grant 2015CFA061, in part by the National Nature Science Foundation of China under Grant 61272278, and in part by Research on Key Technologies of Intelligent Decision-making for Food Big Data under Grant 2018A01038.
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Chen, F., Zhang, C. (2021). ICPM: An Intelligent Compound Prediction Model Based on GA and GRNN. In: Sun, X., Zhang, X., Xia, Z., Bertino, E. (eds) Advances in Artificial Intelligence and Security. ICAIS 2021. Communications in Computer and Information Science, vol 1422. Springer, Cham. https://doi.org/10.1007/978-3-030-78615-1_10
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