1. What You Will Learn
From This
Presentation:
What is Neural Network?
What is Artificial Network Network?
What id Difference Between AAN and BNN?
How Does ANN Work?
How is the AAN Architecture?
What Are Types Of AAN?
What Are The Applications of AAN?
2. What Is Neural Network?
NeuralNeuron
Basic Functional Unit
3. What Is Neural
Network?(Contd.)
Parts Of Neuron:
Dendrite-receive signal from neurons
Soma-sums up incoming signals
Axon-fires signal down the neurons
Synapse-interconnection-one neuron to other
4. What is Artificial Neural
Network?
Biologically inspired Simulations
Used for:
Clustering
Classification
Pattern Recognition
5. Difference Between AAN
and BNN
Parameters BNN ANN
Speed Fast
NanoSecond
s
Slow
Milliseconds
Processing Serial Parallel
Size And
Complexity
Less Size
And
Complexity
More Size
And
Complexity
Storage Replacable Complexe
Interconnec
6. How Is ANN Developed?
Parallel Processing
Learn By Pattern
Essence Program
Use of application-specific multi-chips.
7. Layers Of ANN
Input Layer-receive input-outside world
Output Layer-respond to information-how it learned a
task
Hidden layer-main layer-all processing-transform input-
output
9. How Does ANN Actually
Work?
Large unit work-process information
Weighted Directed Graph
Nodes,directed edges with weights
Information-external world
Input Multiplied By Corresponding weights
Weight input-sum=1
If(sum!=1),basied added
10. How Does ANN Actually
Work?(Contd.)
Adjusting weights and bias
Neural Network-trained first
Training-defined set of rules-learning algorithm
12. Types Of Neural Network
Parameters Types
Based On
Connection
Pattern
Feed Forward
Recurrent
Based On
No.of Hidden
Layer
Single Layer
Multilayer
Based on
Nature of
Weight
Fixed
Adaptive
13. What Are Types Of ANN?
Classification Neural Network
Prediction Neural Network
Clustering Neural Network
Association Neural Network
14. Application Of ANN
Process modeling and control
Machine Diagnostics
Portfolio Management
Target Recognition
Medical Diagnosis
Credit Rating
Targeted Marketing
Voice recognition
Face recognition
Financial Forecasting
Intelligent searching
Fraud detection
15. Advantages Of ANN
Perform tasks-linear function/program cannot
Continue parallel processing-even if somethings fail
Implementated-any application
Doesnot need to be reprogrammed
16. Limitations of ANN
Needs training to be performed
Needs to be emulated
Require-high processing time