探索用于低能耗数据聚合的长短期记忆算法

Gi Hwan Oh
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引用次数: 0

摘要

在错综复杂的低能耗设备中,采用了长短期记忆方法进行数据整合。它能在电力有限的情况下准确、高效地汇总统计数据,方便查看和检索数据,同时最大限度地减少电力浪费。LSTM 规则在弱连接结构中分析、组织和整合庞大的数据集。它采用了递归神经网络来处理数据,尤其是非线性交互。随后,利用内存块对机器的能力进行检查和存储。记忆块保留了数据中扩展的时间连接,有利于自适应和精确的信息聚合。这些记忆块有助于系统利用相关能力进行快速检索。所提出的算法提供了现实的调整功能,如基于像绿色信息聚合那样的辍学的学习率调度和总正则化。这些功能使系统能够减少过度拟合,同时允许对设置进行精确调整。它允许优化算法,在弱结构内提供高度可靠的性能,提高数据聚合技术的能效。标准算法为低功耗系统中的信息聚合提供了高效、精确的解决方案。它利用内存块、自适应调整和高效学习率调度,为评估、检索和聚合准确可靠的信息提供了便利。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Exploring Long Short Term Memory Algorithms for Low Energy Data Aggregation
Long short-term memory methods are employed for data consolidation in intricate low-energy devices. It has enabled accurate and efficient aggregation of statistics in limited electricity settings, facilitating the review and retrieval of data while minimizing electricity wastage. The LSTM rules analyze, organize, and consolidate vast datasets inside weakly connected structures. It has employed a recurrent neural network to handle data processing, particularly nonlinear interactions. The machine's capabilities are subsequently examined and stored utilizing memory blocks. Memory blocks retain extended temporal connections within the data, facilitating adaptive and precise information aggregation. These blocks facilitate the system's ability to shop and utilize relevant capabilities for quick retrieval. The proposed algorithm offers realistic tuning capabilities such as learning rate scheduling and total regularization based on dropout like green information aggregation. These enable systems to reduce over fitting while permitting precise adjustment of the settings. It allows for optimizing the algorithm to provide highly dependable performance within weak structures, enhancing data aggregation techniques' energy efficiency. Standard algorithms provide an efficient, accurate solution for aggregating information in low-power systems. It facilitates evaluating, retrieving, and aggregating accurate and reliable information using memory blocks, adaptive tuning, and efficient learning rate scheduling.
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CiteScore
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