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Classification is made in 2-dimensional space with artificial neural networks learning rules. Perceptron and Delta learning rules are implemented in different layers.

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uzunb/Artificial-Neural-Network-GUI

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Artificial Neural Network Learning Rules

Classification is made in 2-dimensional space with artificial neural networks learning rules.

Learning Factors

  • Initial Weights
  • Steepness of the Activation Function
  • Learning Constant
  • Momentum Method
  • Necessary Number of Hidden Neurons
  • Network Architectures Versus Data Representation
NOTE : Additionally, GUI indicators such as loss charts and period counter negatively affect runtime efficiency.

- SingleCategory SingleLayer Neural Network

- discrete (Perceptron Learning Rule)
- continuos (Delta Learning Rule)
DISCRETE CONTINOUS
SinglePerceptron SingleDelta

- MultiCategory SingleLayer Neural Network

- Discrete (Perceptron Learning Rule) 
- Continous (Delta Learning Rule)
DISCRETE CONTINOUS
MultiCategoryPerceptron MultiCategoryDelta

MultiLayer Neural Network

- Error Back Propagation
MULTILAYER
MultiLayer

License: MIT