摘要
由一名新闻记者-机器人与机器学习日报的工作人员新闻编辑每日新闻-机器学习的研究结果将在一份新报告中讨论。根据NewsRx记者对来自加州洛杉矶的新闻说，“机器”建立了井口反渗透间歇多模式运行的学习模型水净化和脱盐系统，以预测盐通道，硝酸盐排放和渗透通量。该模型基于长短期记忆(LSTM)递归神经网络(RNN)结构，包括一个注意机制，以接近INC Rease模型的监管限度。硝酸盐。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Research findings on Machine Learning are discussed in a new report. Accordingto news originating from Los Angeles, C alifornia, by NewsRx correspondents, research stated, “Machinelearning models w ere developed for intermittent multi-mode operation of a wellhead reverse osmosi swater purification and desalination system to predict salt passage, nitrate pa ssage, and permeate flux.The models, based on long short-term memory (LSTM) rec urrent neural network (RNN) architecture,included an attention mechanism to inc rease model performance in proximity of the regulatory limit fornitrate.”
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