Iterative determination of optimised network architecture of neural network by computer

摘要

The method involves performing iterations by computer. In each iteration step, neural nets of different architectures are generated using a probability vector and an evolutionary method in which each vector component probability is described to form neurons and/or neuron links. For each neural net, a weighting vector is optimised using a training data set and a fitness function determined for the optimised weighting vector. The optimised network architecture of a neural net results from the neural net with a minimal fitness function value if the value lies below a defined level. A further probability vector is determined for generating neural nets in a further iteration step taking into account the value of the fitness function, if the value is not below the defined level.