CPC G06F 30/27 (2020.01) [G06N 3/04 (2013.01); G06N 3/082 (2013.01)] | 16 Claims |
1. A method of construction of a feedforward neural network, the method comprising:
initializing a neural network according to an initial network topology comprising an input layer, at least one hidden layer comprising at least one node, and a set of output nodes, each layer being defined by a set of simultaneously calculable nodes; and
implementing at least one topological optimization phase of the neural network, wherein each topological optimization phase comprises:
at least one additive phase, comprising a modification of the network topology by adding at least one node and/or a connection link between an input of a node of a layer and an output of a node of any preceding layer; and/or
at least one subtractive phase comprising a modification of the network topology by removing at least one node and/or a connection link between nodes of two layers,
wherein each modification of the network topology during an additive or subtractive phase comprises:
for a plurality of candidate modifications of the network topology, estimating a variation between:
a network error for a current topology, and
a network error for a candidate modified topology, the candidate modified topology being a topology resulting from a candidate modification applied to the current topology, and
selecting a modification of the network topology among the plurality of candidate modifications, based on the estimated variations of the network error.
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