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Description: This program simulates a 3 or 4-layer Neural Network, and can be used to
simulate an arbitrary, complex, or non-linear function that would be
difficult to implement by traditional methods. The Back Propagation
method is used "teach" the network the desired function.
Adjacent layers of the net are fully interconnected that is, every
neuron in layer 1 is connected to every neuron in layer 2, and every
neuron in layer 2 is connected to every neuron in layer 3 (1->2, 2->3).
With a 4-layer net, there is further interconnection: 1->2, 1->3, 2->3,
2->4, 3->4.
- This program simulates a 3 or 4-layer Neural Network, and can be used to
simulate an arbitrary, complex, or non-linear function that would be
difficult to implement by traditional methods. The Back Propagation
method is used "teach" the network the desired function.
Adjacent layers of the net are fully interconnected that is, every
neuron in layer 1 is connected to every neuron in layer 2, and every
neuron in layer 2 is connected to every neuron in layer 3 (1->2, 2->3).
With a 4-layer net, there is further interconnection: 1->2, 1->3, 2->3,
2->4, 3->4.
Platform: |
Size: 49152 |
Author: cso |
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Description: Realisation of neural network in Delphi.
Platform: |
Size: 195584 |
Author: Jane |
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