دانلود مقاله ISI انگلیسی شماره 52579
عنوان فارسی مقاله

یک سیستم خبره فازی برای طبقه بندی اتوماتیک سیگنال لرزه

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
52579 2015 15 صفحه PDF سفارش دهید 10360 کلمه
خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.
عنوان انگلیسی
A fuzzy expert system for automatic seismic signal classification
منبع

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

Journal : Expert Systems with Applications, Volume 42, Issue 3, 15 February 2015, Pages 1013–1027

کلمات کلیدی
طبقه بندی سیگنال لرزه ای - استخراج ویژگی سیگنال لرزه - قانون فازی مبتنی بر سیستم خبره - استدلال فازی - مجموعه های فازی
پیش نمایش مقاله
پیش نمایش مقاله یک سیستم خبره فازی برای طبقه بندی اتوماتیک سیگنال لرزه

چکیده انگلیسی

Automatic classification of seismic events is of great importance due to the large amount of data received continuously. Seismic analysts classify events by visual inspection and calculation of event signal characteristics. This process is subjective and demands hard work as well as a significant amount of time and considerable experience. A reliable automatic classification task considerably reduces the effort required and makes classification faster and more objective. The aim of this study is to develop a fuzzy rule based expert classification system that is able to imitate human reasoning and incorporate the analyst’s knowledge of seismic event classification. The fundamental idea behind using this approach was motivated by the way in which human analysts classify seismic events based on a set of experiential rules. Additionally, this approach was chosen due to its interpretability and adjustability, as well as its ability to manage the complexity of real data. Relevant discriminant features are extracted from event signal. Using these features, the classification system was built based on the vote by multiple rule fuzzy reasoning method with three types of rules. Comparison of this method with the single winner classical fuzzy reasoning model was carried out. Classification results on real seismic data showed the robustness of the classifier and its capability to operate in on-line classification.

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