Perceptron
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
- Perceptron
| 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 |
Sub-sections
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