Neural networks

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Neural networks

  1. 1. Neural networksNeural networks
  2. 2. PartsParts:: The Biological Inspiration. ANN (Artificial Neural Nets). STATISTICA Neural Networks.
  3. 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. 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. 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. 6. ANN (Artificial Neural Nets).ANN (Artificial Neural Nets).
  7. 7. STATISTICA Neural Networks Program package for creation and teaching neural networks. StatSoft ® Russia
  8. 8. Amazing simplicity  Adviser in design’s problem  Solver wizard Abundant visualization tools STATISTICA Neural Networks
  9. 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. 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. 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. 12. STATISTICA Neural Network: complementary function Genetic algorithm of choosing input data Regularizing of weights Sensitivity analysis Possibility to make a loss matrix
  13. 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. 14. We have discussedWe have discussed:: The Biological neural networks Artificial Neural Nets Features of program package «STATISTICA Neural Networks»
  15. 15. Neural networksNeural networks E-mail:

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