تعيين رفتار طبقه‌بندها با كليشه تصميم مبتني بر مدل مخفي ماركوف
محورهای موضوعی : مهندسی برق و کامپیوتر
1 - دانشگاه تربیت معلم سبزوار
کلید واژه: رفتار طبقه‌بندهاشناسايي الگوكليشه تصميممدل مخفي ماركوفشبكه عصبي پس‌انتشار خطا,
چکیده مقاله :
مطالعه رفتار طبقه‌بندها از ديدگاه بررسي خطاهای آنها و ارائه راه حل مناسب براي كاهش خطا و افزايش كارآيي طبقه‌بندها مورد توجه است. عملكرد ضعيف سيستم شناسايي به دلايل تعداد كم نمونه‌هاي يادگير، نويز در داده‌ها، استفاده از ويژگي‌هاي شكننده به دليل عدم آگاهي كامل و تسلط كافي بر نوع الگو و استفاده از عملگرهای نامناسب در تعيين پاسخ سيستم، كاهش نرخ شناسايي در تصميم‌گيري نهايي را به دنبال دارد. با ارائه مدل مناسب آماري براي رفتار يا پاسخ يك سيستم شناسايي، مي‌توان عملكرد سيستم شناسايي را بهبود داد. در اين مقاله يك كليشه تصميم جديد كه با استفاده از مدل مخفي ماركوف ايجاد مي‌شود، رفتار نرون‌هاي يك شبكه عصبی پس‌انتشار خطا را، مدل مي‌كند. در روش‌هاي موجود ارتباط بين نرون‌ها و تأثير متقابل آنها در پاسخ به يك الگو مد نظر قرار نمي‌گيرد. ولي عملاً نرون‌هاي يك شبكه عصبي يا اجزاي يك طبقه‌بند، با هم بيان‌كننده عملكرد آن در قبال يك الگو هستند. بنابراين، با استفاده از يك كليشه تصميم جديد مبتني بر مدل مخفي ماركوف، ارتباط بين نرون‌هاي شبكه عصبي و نحوه پاسخ آن به نمونه‌هاي يادگير، مدل مي‌گردد تا از آن در شناسايي الگوهاي جديد استفاده شود. روش جديد در مدل‌كردن رفتار طبقه‌بند، در سه كاربرد شناسايي ارقام دست‌نويس فارسي، تشخيص ترافيك عادي در شبكه‌هاي اطلاع‌رساني و شناسايي نوع وسيله نقليه آزمون مي‌شود و افزايش قابل توجهي در نرخ شناسايي طبقه‌بند به دست مي‌دهد.
Studying of classifier behavior is interested from viewpoint of error checking and presentation of suitable solution for decreasing error rates and decreasing performance. Weakness operation of recognition system is because of small number of training samples, noisy samples, unsuitable extracted features, method of determining of system response. Presentation of suitable model for behavior or response of recognition system, we can improve operation of recognition system. In this paper, a new hidden Markov model based decision template is generated for modeling of neurons behavior in neural network. In existing methods, relation of neurons and interaction between them is not studied whereas; response of neural network includes response value of all neurons. So, relations of neurons are modeled using new hidden Markov decision templates. This method is used into three applications include recognition of Farsi number images, normal traffic in internet network, and recognition of types of vehicles. Increasing performance of neural network indicates to superiority of the proposed system.
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