Образец работы студента/Just a sample student's slide pack

1. Neural networksNeural networks

2. PartsParts::
The Biological Inspiration.
ANN (Artificial Neural Nets).
STATISTICA Neural Networks.

3. ModernModern biologybiology::
•Cell - element, which is able to process the
information
•Neuron – element of brain’s cellular
construction
•Neuron implements an information reception and
transfer as impulses of nervous activity
•Impulse’s character is electrochemical
• Axon – process of nerve cell, which is impulse’s
tract from the body of cell to all organs and the
others nerve cells.

4. IInteresting facts:nteresting facts:
Body of cell measures 3 - 100 microns.
The giant squid’s axon’s thickness is 1 millimetre
and its length is several metres.
General number of neurons in human’s central
nervous system is 100.000.000.000.
Every cell is connected with 10.000 others
neurons.

5. ANN (Artificial Neural Nets).
Units & WeightsUnits & Weights
Units
◦ Sometimes
notated with unit
numbers
Weights
◦ Sometimes give
by symbols
◦ Sometimes given
by numbers
◦ Always represent
numbers
◦ May be integer or
real valued
1
2
3
4
0.3
-0.1
2.1
-1.1
1
1
Unitnumbers
Unitnumber
1
2
3
4
W1,1
W1,2
W1,3
W1,4

6. ANN (Artificial Neural Nets).ANN (Artificial Neural Nets).

7. STATISTICA
Neural Networks
Program package for creation and
teaching neural networks.
StatSoft
®
Russia

8. Amazing simplicity
 Adviser in design’s problem
 Solver wizard
Abundant visualization tools
STATISTICA Neural Networks

9. STATISTICA Neural Networks:
work with data
Different input data :
– number variable;
– Input and output parameters;
– Subset of researches
File’s import, usage of clipboard.
Embedded algorithms.

10. STATISTICA Neural Networks:
work with net
Quality rating:
– regression’s statistics;
– classification’s statistics;
Scanning of data and different researches.
The creation of forecast.

11. STATISTICA Neural Networks:
network building
Create and storage network sets.
Choice of networks type:
– Multilayer perceptron (MLP);
– Radial Basis Function (RBF);
– Kohonen network.
Setting error function and activate function for
different layers.
Access to weights for all neurons .

12. STATISTICA Neural Network:
complementary function
Genetic algorithm of choosing input data
Regularizing of weights
Sensitivity analysis
Possibility to make a loss matrix

13. STATISTICA Neural Networks:
creation application
Cooperation with sistem STATISTICA: data and
diagram communications.
Application programming interface (API) for
creation applications,which are functioning in
Visual Basic and C++.

14. We have discussedWe have discussed::
The Biological neural networks
Artificial Neural Nets
Features of program package
«STATISTICA Neural Networks»

15. Neural networksNeural networks
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