Backpropagation

 

The backpropagation algorithm is a supervised learning method for MLP (multi-layer perceptron) networks with sigmoidal activation units. The goal is to find a good mapping from input data to output data. When a new data record is fed to a network, the network provides a good mapping to the output space by using the intrinsic structure of the training set. This implementation allows the user to use several different training methods. Although the basic training algorithm is slow compared to other methods, it can provide, in some cases, a better representation of the training set.

• The basic learning rule
• The basic learning rule with a momentum term
• Global adaptive training step length
• The Matlab neural network toolbox style training
• The Silva & Almeida training
• The RPROP training
• The Levenberg-Marquard training
• Importing a new data to a trained network
• Visualizing an MLP network