Backpropagation Net
The Backpropagation Net was first introduced by G.E. Hinton, E. Rumelhart and R.J. Williams in 1986 and is one of the most powerful neural net types. It has the same structure as the Multi-Layer-Perceptron and uses the backpropagation learning algorithm.
Sample Structure
- Backpropagation Neural Network
| Type | feedforward |
| Neuron layers | 1 input layer, 1 or more hidden layer(s), 1 output layer |
| Input value types | binary |
| Activation function | sigmoid |
| Learning method | supervised |
| Learning algorithm | backpropagation |
| Mainly used in | complex logical operations, pattern classification, speech analysis |
Sub-sections
Copyright (c) 1996-2024 Neural Networks with Java - Jochen Fröhlich. All rights reserved.
