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PRESENTED BY
PANKAJ SHARMA
SEMINAR
COMPARISON BETWEEN NEURAL
NETWORKS AND COMPUTER
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What is a Neural Network?
Neural network is collection of highlyinterconnected processing
elements calledneurons.
Neural networks are inspired by the waybiological nervous system
such as brainprocess information.
Neural networks learn by example.
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Basic structure of Brain
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Specifications of brain
Number of Neurons 100 billion
Number of
synapses/neuron
1000
Total number of synapses 100000 billion
Operations/s/neuron 100
Total operations 10000 trillion
Brain weight 1.5 kg
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Characteristics of ANN
Parallel computation: program is executedsimultaneously.
Robustness: insensitive to partial inputs.
Self learning: algorithm is created itself.
Fault tolerance: output is not affected ifone or two neuron are
damaged.
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Basic model of an ANNx1*w1 + x2*w2 + > th
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Learning in ANN
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Precautions before learning process
1. Decide the function of neural network to beperformed.
2. Make a complete set of input and outputpatterns.
3. Determine the number of layers in networkand number of nodes
per layer.
4. Select the appropriate threshold value.5. Determine the
algorithm termination
criteria.
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Applications of neural networks
1. In signal processing.
2. Image data processing.
3. Communication systems.
4. Intelligent control.5. Optimization techniques.
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Computer: essential characteristics
The problem to be solved should have somemathematical
solution.
All the steps for solving any problem shouldbe explicitly
specified.
The data should have a precise format.
There should not be any memory crash.
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Steps in computing
1. Fetch an instruction from memory.
2. Fetch any data required by the instructionfrom memory.
3. Execute the instruction.
4. Store the result in memory.
5. go to step 1
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Computer: advantage and limitation
1. Advantage:
Fast arithmetic.
Do precisely what the programmer wants to do.
Makes life easier.
2. Limitation :
Not fault tolerance.
Cannot solve a problem without a mathematicalsolution.
No parallelism.
Cannot adapt according to circumstances.
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Comparison between computer and
neural network1. Problem solving approach : in most of cases
computer uses top -
down approach in problem solving. Whereas neural network
usesbottom up approach in problem solving.
2. Way of functioning : computers function logically with a set
of rulesand calculations whereas neural networks can function via
images ,pictures and concepts.
3. Self programming : computers need some algorithm for
problem
solving , whereas neural network learns by itself.
4. Speed : speed of computer depends on various aspects of
processor, whereas neural network use chips for different
applications.
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