Construct the prediction model for China agricultural output value based on the optimization neural network of fruit fly optimization algorithm

• Published in: Future generation computer systems Vol. 86; pp. 663 - 669
• Main Authors: Han, Shi-Zhuan, Pan, Wen-Tsao, Zhou, Ying-Ying, Liu, Zong-Li
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.09.2018
• Subjects: Chaos Fruit Fly Optimization Algorithm; Fruit Fly Optimization Algorithm; General Regression Neural Network; Improved Fruit Fly Optimization Algorithm; Total agricultural production value; Total agricultural production value; General Regression Neural Network; Fruit Fly Optimization Algorithm; Improved Fruit Fly Optimization Algorithm; Chaos Fruit Fly Optimization Algorithm
• ISSN: 0167-739X; 1872-7115
• DOI: 10.1016/j.future.2018.04.058

Since agriculture is the foundation of a country and the industry that people depend on for life, it is particularly important for the development of national economy, and it has a higher output value than forestry, fishery and animal husbandry, so it occupies a very important position in the economic development of a country. The aim of this paper is to strengthen the capacity of prediction mode for total agricultural output value. This paper provides relevant government departments a reference and solves the problem of the lack of predictive ability of prediction mode for total agricultural output value in previous study. Different from previous literature, this paper adopts the new CFOA to optimize the parameters of GRNN, which contains innovative and reference value in some degree. Besides the way to validate this new model is to take the agricultural output value of the past years as a research sample and test it repeatedly. The study results have indicated that the total agricultural production value accounts for a higher proportion of agriculture, forestry, fishery and animal husbandry and the proportion tends to decline year by year; it can be found through 4 evaluation indexes that the prediction model that optimizes the smoothing parameters of GRNN through CFOA has a better predictive ability than the other two prediction models. •The aim of this paper is to strengthen the capacity of prediction mode for total agricultural output value.•This paper solves the problem of the lack of predictive ability of prediction mode for total agricultural output value in previous study.•Different from previous literature, this paper adopts the new CFOA to optimize the parameters of GRNN, which contains innovative.•The study results show that the total agricultural production value accounts for a higher proportion and the proportion tends to decline year by year.