The Perceptron was first introduced by F. Rosenblatt in 1958.

It is a very simple neural net type with two neuron layers that accepts only binary input and output values (0 or 1). The learning process is supervised and the net is able to solve basic logical operations like AND or OR. It is also used for pattern classification purposes.

More complicated logical operations (like the XOR problem) cannot be solved by a Perceptron.

Sample Structure


Type feedforward
Neuron layers 1 input layer, 1 output layer
Input value types binary
Activation function hard limiter
Learning method supervised
Learning algorithm Hebb learning rule
Mainly used in simple logical operations, pattern classification
Neural Net Components in an Object Oriented Class Structure