Modeling and FPGA Implementation of ANN Based Electronic Circuits
| AUTHOR | Shukr Mahmood, Basil; Merza Al-Abasy, Marwah Ezzulddin |
| PUBLISHER | Noor Publishing (12/16/2020) |
| PRODUCT TYPE | Paperback (Paperback) |
Description
In this book, a new approach is proposed to build neural network architectures. Previous works are used back-propagation. The major limitation of this network that it can only learn an input - output mapping which is static. Recurrent neural networks (RNNs) have features that well define dynamic systems which have attracted the attention of researches in this field. Generally, recurrent neural network requires less neurons in its structure and less computation time. Also, they show high immunity against external noise. In this book, a new approach is proposed to build neural network architectures. Previous works are used back-propagation. The major limitation of this network that it can only learn an input - output mapping which is static. Recurrent neural networks (RNNs) have features that well define dynamic systems which have attracted the attention of researches in this field. Generally, recurrent neural network requires less neurons in its structure and less computation time. Also, they show high immunity against external noise.
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Product Format
Product Details
ISBN-13:
9786202791090
ISBN-10:
6202791098
Binding:
Paperback or Softback (Trade Paperback (Us))
Content Language:
English
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Page Count:
164
Carton Quantity:
44
Product Dimensions:
6.00 x 0.38 x 9.00 inches
Weight:
0.55 pound(s)
Country of Origin:
US
Subject Information
BISAC Categories
Technology & Engineering | Electronics - General
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publisher marketing
In this book, a new approach is proposed to build neural network architectures. Previous works are used back-propagation. The major limitation of this network that it can only learn an input - output mapping which is static. Recurrent neural networks (RNNs) have features that well define dynamic systems which have attracted the attention of researches in this field. Generally, recurrent neural network requires less neurons in its structure and less computation time. Also, they show high immunity against external noise. In this book, a new approach is proposed to build neural network architectures. Previous works are used back-propagation. The major limitation of this network that it can only learn an input - output mapping which is static. Recurrent neural networks (RNNs) have features that well define dynamic systems which have attracted the attention of researches in this field. Generally, recurrent neural network requires less neurons in its structure and less computation time. Also, they show high immunity against external noise.
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