Multi-Layer-Perceptron

The Multi-Layer-Perceptron was first introduced by M. Minsky and S. Papert in 1969.

It is an extended Perceptron and has one ore more hidden neuron layers between its input and output layers.

Due to its extended structure, a Multi-Layer-Perceptron is able to solve every logical operation, including the XOR problem.

Type	feedforward
Neuron layers	1 input layer, 1 or more hidden layer(s), 1 output layer
Input value types	binary
Activation function	hard limiter / sigmoid
Learning method	supervised
Learning algorithm	delta learning rule, backpropagation (mostly used)
Mainly used in	complex logical operations, pattern classification

Sub-sections