人工神经网络和支持向量机在需水量预测中的应用 时间序列预测

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摘要翻译：
水在许多物理过程中起着关键作用，最重要的是在维持人类生命、动物生命和植物生命方面。因此，供水实体有责任按照消费者所需的费率提供清洁和安全的水。因此，有必要实施可用于预测短期和长期用水需求的机制和系统。日益增长的计算智能技术被认为是动态现象建模的有效工具。本文的主要目的是比较两种计算智能技术在需水量预测中的效率。所比较的技术是人工神经网络(ANNs)和支持向量机(SVMs)。在这项研究中，我们观察到ANNs比SVM表现得更好。这种性能是以两者的概括能力来衡量的。
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英文标题：
《Artificial Neural Networks and Support Vector Machines for Water Demand
  Time Series Forecasting》
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作者：
Ishmael S. Msiza, Fulufhelo V. Nelwamondo and Tshilidzi Marwala
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最新提交年份：
2007
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  Water plays a pivotal role in many physical processes, and most importantly in sustaining human life, animal life and plant life. Water supply entities therefore have the responsibility to supply clean and safe water at the rate required by the consumer. It is therefore necessary to implement mechanisms and systems that can be employed to predict both short-term and long-term water demands. The increasingly growing field of computational intelligence techniques has been proposed as an efficient tool in the modelling of dynamic phenomena. The primary objective of this paper is to compare the efficiency of two computational intelligence techniques in water demand forecasting. The techniques under comparison are the Artificial Neural Networks (ANNs) and the Support Vector Machines (SVMs). In this study it was observed that the ANNs perform better than the SVMs. This performance is measured against the generalisation ability of the two.
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PDF链接：
https://arxiv.org/pdf/0705.0969

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关键词：时间序列预测 人工神经网络 支持向量机 时间序列 神经网络 人工神经网络 研究 计算 智能 life