摘要翻译:
本文研究了用不同的人工智能方法来预测蒸汽发生器的几个连续变量的值。目的是确定不同的人工智能方法如何在给定的数据集上进行预测。评价的人工智能方法是神经网络、支持向量机和自适应神经模糊推理系统。研究的神经网络类型有多层感知和径向基函数。将贝叶斯和委员会技术应用于这些神经网络。在MATLAB中对所考虑的每一种人工智能方法进行了仿真。仿真结果表明,所有的人工智能方法都能够合理、准确地预测蒸汽发生器的数据。然而,自适应神经模糊推理系统在精度和易实现性方面优于其他方法,同时仍然获得了快速的执行时间和合理的训练时间。
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英文标题:
《Artificial Intelligence Techniques for Steam Generator Modelling》
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作者:
Sarah Wright and Tshilidzi Marwala
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最新提交年份:
2008
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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:
This paper investigates the use of different Artificial Intelligence methods to predict the values of several continuous variables from a Steam Generator. The objective was to determine how the different artificial intelligence methods performed in making predictions on the given dataset. The artificial intelligence methods evaluated were Neural Networks, Support Vector Machines, and Adaptive Neuro-Fuzzy Inference Systems. The types of neural networks investigated were Multi-Layer Perceptions, and Radial Basis Function. Bayesian and committee techniques were applied to these neural networks. Each of the AI methods considered was simulated in Matlab. The results of the simulations showed that all the AI methods were capable of predicting the Steam Generator data reasonably accurately. However, the Adaptive Neuro-Fuzzy Inference system out performed the other methods in terms of accuracy and ease of implementation, while still achieving a fast execution time as well as a reasonable training time.
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PDF链接:
https://arxiv.org/pdf/0811.1711