کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
8901489 | 1631737 | 2018 | 17 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Efficient methods of initializing neuron weights in self-organizing networks implemented in hardware
ترجمه فارسی عنوان
روشهای کارآمد برای راه اندازی وزنهای نورون در شبکههای خودسازمانده در سختافزار
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
ریاضیات
ریاضیات کاربردی
چکیده انگلیسی
The investigations have shown that Self-Organizing Maps (SOMs) in many situations may be trained without any initialization (with zeroed weights). This is possible due to the neighborhood mechanism that to some degree stimulates the neurons belonging to the SOM. We present selected results of several thousands simulations for different topologies of the SOM, for different neighborhood functions and two distance measures between the learning patterns and neurons in the input data space. Simulations were performed for initial values of the weights equal to zero, for small values (up to 1% of full scale range) and for neurons randomly distributed over the overall input data space. The results in most cases are comparable that allows to reduce the complexity of the SOM implemented in the CMOS technology.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Applied Mathematics and Computation - Volume 319, 15 February 2018, Pages 31-47
Journal: Applied Mathematics and Computation - Volume 319, 15 February 2018, Pages 31-47
نویسندگان
Marta Kolasa, RafaÅ DÅugosz, Tomasz TalaÅka, Witold Pedrycz,