案例推理系统中检索过程的优化与神经网络自适应

I. Lodhi, K. Hasan, U. Hasan, N. Mahmood, T. Yoshida, M. A. Anwar
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引用次数: 2

摘要

基于案例推理系统(CBR)的检索过程分为两步。它以问题描述开始,并在检索到最匹配的先前案例时结束。为了优化检索过程,需要增强这两个过程。这项研究工作探索了XML(可扩展标记语言)作为case描述语言的使用。另一个目标是确定在优化过程中起主要作用的因素。本文还对人工神经网络在CBR系统自适应过程中的应用进行了实验研究。采用不同配置的反向传播前馈神经网络,对该技术在案例自适应中的应用进行了实证分析。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimizing retrieval process and using neural networks for adaptation process in case based reasoning systems
The retrieval process in case based reasoning systems (CBR) is a two-step process. It starts with a problem description and ends when a best matching previous case(s) has/have been retrieved. To optimize the retrieval process, enhancement of both processes is required. This research work explores the use of XML (Extensible Markup Language) as a case descriptive language. An additional goal is to identify factors which play a major role in the optimization process. This work also presents an experimental investigation concerning the use of artificial neural networks in the adaptation process of CBR systems. A backpropagation feedforward neural network in different configurations, has been employed to carry out empirical analysis of using this technique for case based adaptation.
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