C# Class SharpNeat.Phenomes.NeuralNets.FastAcyclicNetwork

A neural network implementation for acyclic networks. Activation of acyclic networks can be far more efficient than cyclic networks because we can activate the network by propagating a signal 'wave' from the input nodes through each depth layer through to the output nodes, thus each node requires activating only once at most, whereas in cyclic networks we have to activate each node multiple times and we must have a scheme for determining when to stop activating. Algorithm Overview. 1) The nodes are assigned a depth number based on how many connection hops they are from an input node. Where multiple paths to a node exist the longest path determines the node's depth. 2) Connections are similarly assigned a depth value which is defined as the depth of a connection's source node. Note. Steps 1 and 2 are actually performed by FastAcyclicNetworkFactory. 3) Reset all node activation values to zero. This resets any state from a previous activation. 4) Each layer of the network can now be activated in turn to propagate the signals on the input nodes through the network. Input nodes do no apply an activation function so we start by activating the connections on the first layer (depth == 0), this accumulates node pre-activation signals on all of the target nodes which can be anywhere from depth 1 to the highest depth level. Having done this we apply the node activation function for all nodes at the layer 1 because we can now guarantee that there will be no more incoming signals to those nodes. Repeat for all remaining layers in turn.