C++ Neural Networks and Fuzzy Logic: Preface

Many Layer Networks

Kunihiko Fukushima’s Neocognitron, noted for identifying handwritten characters, is an example of a

network with several layers. Some previously mentioned networks can also be multilayered from the addition

of more hidden layers. It is also possible to combine two or more neural networks into one network by

creating appropriate connections between layers of one subnetwork to those of the others. This would

certainly create a multilayer network.

Connections Between Layers

You have already seen some difference in the way connections are made between neurons in a neural

network. In the Hopfield network, every neuron was connected to every other in the one layer that was present

in the network. In the Perceptron, neurons within the same layer were not connected with one another, but the

connections were between the neurons in one layer and those in the next layer. In the former case, the

connections are described as being lateral. In the latter case, the connections are forward and the signals are

fed forward within the network.

Two other possibilities also exist. All the neurons in any layer may have extra connections, with each neuron

connected to itself. The second possibility is that there are connections from the neurons in one layer to the

neurons in a previous layer, in which case there is both forward and backward signal feeding. This occurs, if

feedback is a feature for the network model. The type of layout for the network neurons and the type of

connections between the neurons constitute the architecture of the particular model of the neural network.

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C++ Neural Networks and Fuzzy Logic:Preface

Other Two−layer Networks

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