دانلود مقاله ISI انگلیسی شماره 52607
عنوان فارسی مقاله

آموزش ساختار برای باور قانون محور سیستم خبره : مطالعه مقایسه ای

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
52607 2013 14 صفحه PDF سفارش دهید محاسبه نشده
خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.
عنوان انگلیسی
Structure learning for belief rule base expert system: A comparative study
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Knowledge-Based Systems, Volume 39, February 2013, Pages 159–172

کلمات کلیدی
باور قانون محور - آغازگر - یادگیری ساختار - کاهش ابعاد - تجزیه و تحلیل مولفه های اصلی
پیش نمایش مقاله
پیش نمایش مقاله آموزش ساختار برای باور قانون محور سیستم خبره : مطالعه مقایسه ای

چکیده انگلیسی

The Belief Rule Base (BRB) is an expert system which can handle both qualitative and quantitative information. One of the applications of the BRB is the Rule-base Inference Methodology using the Evidential Reasoning approach (RIMER). Using the BRB, RIMER can handle different types of information under uncertainty. However, there is a combinatorial explosion problem when there are too many attributes and/or too many alternatives for each attribute in the BRB. Most current approaches are designed to reduce the number of the alternatives for each attribute, where the rules are derived from physical systems and redundant in numbers. However, these approaches are not applicable when the rules are given by experts and the BRB should not be oversized. A structure learning approach is proposed using Grey Target (GT), Multidimensional Scaling (MDS), Isomap and Principle Component Analysis (PCA) respectively, named as GT–RIMER, MDS–RIMER, Isomap–RIMER and PCA–RIMER. A case is studied to evaluate the overall capability of an Armored System of Systems. The efficiency of the proposed approach is validated by the case study results: the BRB is downsized using any of the four techniques, and PCA–RIMER has shown excellent performance. Furthermore, the robustness of PCA–RIMER is further verified under different conditions with varied number of attributes.

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