A Water-saving Irrigation Decision-making Model for Greenhouse Tomatoes based on Genetic Optimization T-S Fuzzy Neural Network

文献类型： 外文期刊

第一作者： Chen, Zhili

作者： Chen, Zhili;Chen, Zhili;Zhao, Chunjiang;Wu, Huarui;Miao, Yisheng;Zhao, Chunjiang;Wu, Huarui;Miao, Yisheng

作者机构：

关键词： T-S fuzzy neural network; genetic optimization; krill herd; irrigation decision; greenhouse tomatoes; Internet of Things

期刊名称：KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS （ 影响因子：0.858； 五年影响因子：0.746 ）

ISSN： 1976-7277

年卷期： 2019 年 13 卷 6 期

页码：

收录情况： SCI

摘要： In order to improve the utilization of irrigation water resources of greenhouse tomatoes, a water-saving irrigation decision-making model based on genetic optimization T-S fuzzy neural network is proposed in this paper. The main work are as follows: Firstly, the traditional genetic algorithm is optimized by introducing the constraint operator and update operator of the Krill herd (KH) algorithm. Secondly, the weights and thresholds of T-S fuzzy neural network are optimized by using the improved genetic algorithm. Finally, on the basis of the real data set, the genetic optimization T-S fuzzy neural network is used to simulate and predict the irrigation volume for greenhouse tomatoes. The performance of the genetic algorithm improved T-S fuzzy neural network (GA-TSFNN), the traditional T-S fuzzy neural network algorithm (TSFNN), BP neural network algorithm(BPNN) and the genetic algorithm improved BP neural network algorithm (GA-BPNN) is compared by simulation. The simulation experiment results show that compared with the TSFNN, BPNN and the GA-BPNN, the error of the GA-TSFNN between the predicted value and the actual value of the irrigation volume is smaller, and the proposed method has a better prediction effect. This paper provides new ideas for the water-saving irrigation decision in greenhouse tomatoes.

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