基于LSTM的离散输入模型预测控制灌溉调度
LSTM-based model predictive control with discrete inputs for irrigation scheduling
摘要：
从植物质量和水资源保护的角度来看，发展精心设计的灌溉调度方法是可取的。在这篇文章中，模型预测控制（MPC）与离散执行器的灌溉调度，其中的长短期记忆（LSTM）模型的土壤-水-大气系统用于评估的目标，以确保最佳的水分吸收在作物，同时最小化总耗水量和灌溉成本。一个启发式的方法，涉及一个sigmoid函数在这个框架中使用，以提高调度器的计算效率。调度方案被施加到均匀和空间可变的领域和结果表明，基于LSTM的MPC与离散执行器是能够规定最佳或接近最佳的灌溉计划，是典型的灌溉实践。
英文摘要：
The development of well-devised irrigation scheduling methods is desirable from the perspectives of plant quality and water conservation. In this article, a model predictive control (MPC) with discrete actuators is developed for irrigation scheduling, where a long short-term memory (LSTM) model of the soil-water-atmosphere system is used to evaluate the objective of ensuring optimal water uptake in crops while minimizing total water consumption and irrigation costs. A heuristic method involving a sigmoid function is used in this framework to enhance the computational efficiency of the scheduler. The scheduling scheme is applied to homogeneous and spatially variable fields and the results indicate that the LSTM-based MPC with discrete actuators is able to prescribe optimal or near-optimal irrigation schedules that are typical of irrigation practice.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
