تشخيص خودکار گفتار رقمی با استفاده از شبکه عصبی اسپايکينگ عميق بر اساس وزن دهی فازی
محورهای موضوعی : مهندسی کامپیوتر و فناوری اطلاعاتملیکا حامیان 1 , کریم فایز 2 , سهیلا نظری 3 , ملیحه ثابتی 4
1 - دانشجوی دکتری
2 - گروه مهندسی کامپیوتر، واحد تهران شمال، دانشگاه آزاد اسلامی، تهران، ایران
3 - گروه مهندسی کامپیوتر، واحد تهران شمال، دانشگاه آزاد اسلامی، تهران، ایران
4 - Department of Computer Engineering, North Tehran Branch, Islamic Azad University, Tehran, Iran
کلید واژه: سیستم تشخیص ارقام, شبکه عصبی اسپایکینگ, سیستم وزن¬دهی فازی,
چکیده مقاله :
علیرغم پیشرفتهای انجام شده در طراحی شبکههای عصبی اسپایکینگ، آموزش این سیستمها برای طبقهبندی و کاربردهای هوش مصنوعی از چالشهای پیشرو برای طراحی آنهاست. در این مقاله ما یادگیری نظارت‌شده را در شبکههای عصبی اسپایکی برای مساله تشخیص و طبقهبندی رقم از روی سیگنال های گفتار، بررسی کردهایم. در این روش، قانون یادگیری سیستم وزندهی فازی با انعطافپذیری وابسته به زمان اسپایک ادغام میشوند. قانون انعطافپذیری وابسته به زمان اسپایک ترکیب شده با سیستم وزندهی فازی، توزیع وزن تصادفی را ایجاد میکند که در آن محدوده پنجره انعطافپذیری وابسته به زمان اسپایک کنترل میشود. شبکه عصبی اسپایکینگ از یک مجموعه نورون آموزشی با وزندهی فازی برای کاهش تعداد وزنهای هر نورون، در مرحله آموزش استفاده میکند که در آن دادههای مرتبط با تمام کلاسها به این نورونها جهت تعیین وزنهای آموزش و تخمین آستانه با کمک الگوریتم اسب وحشی، اعمال میشود. سپس این قانون وزن‌ها، به نورون‌های لایههای مختلف داده میشوند تا شباهت‌ها را در ویژگی‌های استخراج شده در بین کلاسها به عنوان تابع هدف، منعکس نماید. نتایج روش پیشنهادی، دقت طبقهبندی 17/98% در پایگاه داده آزمایشی TIDIGITS را نشان میدهد.
Despite the progress made in the design of spiking neural networks (SNN), training these systems for classification and artificial intelligence applications is one of the upcoming challenges for their design. In this paper, we have investigated supervised learning in SNNs for the problem of digit recognition and classification from speech signals. SNN training is done using fuzzy logic. In this method, the learning rule integrates Fuzzy Weighting System (FWS) with Spike Time Dependent Flexibility (STDP). SNN uses a set of training neurons with fuzzy weighting to reduce the number of weights of each neuron in the training phase, in which the data related to all classes are fed to these neurons to determine the training weights and threshold estimation with the help of the Wild Horse Algorithm (WHO). Then, these rule weights are given to the neurons of different layers to reflect the similarities in the extracted features among the classes as an objective function. A case study has been carried out on a set of audio signal data for digit classification. Our network achieved a classification accuracy of 98.17% on the TIDIGITS test database.
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