Cellular Neural Network

8/11/2019 Cellular Neural Network.....

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CELLULAR NEURAL NETWORK

VISHAL DWIVEDI

ROLL NO.1109131118


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Similarity between Neuron and Cell


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ABOUT CNN IN GENERAL

In 1988 papers from Lion O. Chua introduced the

concept of Cellular Neural Network .

CNN can be defined as 2D or 3D arrays of mainly

locally connected non-linear dynamical system called

cells whose dynamics are functionally determined by

a small set of parameters which control the cells inter-

connection strength(Chua).


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Rectangular Grid showing CNN


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Templates

The functionality of the CNN array can be

controlled by the cloning templateA, B, I

WhereA and B are (2r+1) x (2r+1) real

matrices

I is a scalar number in two dimensional

cellular neural networks.


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Logical AND Template

This template creates

the pixel wise AND of

the initial state and the

input The progression of

images shows the input

and initial image,

followed by the output

at a series of times


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Classification of CNN

Continuous Time CNN

Discrete Time CNN


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Continuous Time CNN

Chua & Yangs CNN(CY-CNN)

Linear Cloning Template CNN

Full Range CNN Linear Threshold/Feed-Forward CNN

Polynomial CNN

Non-uniform Processor CNN


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Several Other CNN

Discrete time CNN

Multi-valued CNN

CNN with Local Logic(CNNLL)


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Why Use CNN?

CNN with their remarkable ability to derive meaning

from complicated data can be used to extract patterns

and detect trends that is too difficult to be detected by

human or other computer technique.Adaptive learning

Self Organisation

Real Time Operation

Fault Tolerance via Redundant Information Coding


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CNN Applications

CNN Processors are versatile platform used for variety

of applications such as

Image Processing

Generation and Analysis of patterns and textures

Chaotic Communication using CNN processors


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Planning Flowchart of Motion


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CNN versus Conventional Computers

Conventional Computer

Use algorithmic

approach

These are predictable

Speed is slow in

comparison

CNN

Mimic the human brain

These are not

predictable

Speed is the key factor

for CNN processors


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CONCLUSION

A brief overview of CNN is given.

Comparison of CNN and other computational

technique is discussed and it is concluded that they

are not in competition but are complement to eachother.

CNN do not perform miracles ,but if they are

used sensibly can produce some amazing results.


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Documents

Cellular Neural Network