Neural network implementation

Type :

Term papers

Pages :

4 pages

Format :

.pdf

Published date :

10/08/2009

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Summary :

 
 

Table of Contents Neural network implementation Table of Contents

 
  1. Abstract
  2. Introduction
  3. Types of neural networks
  4. Implementing neural networks
    1. Basic network structure
  5. Conclusion
  6. References

Abstract

CMOL circuits seem more suitable for the implementation of naturally defect-tolerant architectures, including artificial neural networks. neural networks designed using CMOL are named as "Cross Nets" and they may eventually exceed the mammal brain in a real density, at much higher speed and acceptable power consumption. In this report implementation of artificial neural networks (ANNs) using CMOL technology is explained. The true power and advantage of neural networks lies in their ability to represent both linear and non-linear relationships and in their ability to learn these relationships directly from the data being modeled. A mammalian brain recognizes a complex visual image with very high fidelity in approximately 100milliseconds. Since the elementary process of neural cell-to-cell communication in the cerebral cortex takes approximately10 milliseconds, this means that this task is completed in just a few "ticks" of the cortical circuitry

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About the author :

pencil image Vijay P. Director of Shri HDG MBA College
Level :General public Study : Management School/University : Saurashtra University

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