Optimized artificial neural networks

A method of optimizing the structure of an artificial neural network, comprising:

• defining a neural network having an initial architecture comprised of a plurality of input neurons, output neurons and hidden neurons, and a plurality of signal transmission paths for applying output signals from said input neurons to said hidden neurons and for applying output signals from said hidden neurons to said output neurons;
• defining a symbol string representing the architecture of the neural network;
• providing a set of neural net input-output pairs including a set for training and a set for evaluation;
• training the neural network by supervised learning using the training set of input-output pairs;
• evaluating the trained neural network using the evaluation set of input-output pairs;
• modifying the symbol string representation of the neural network architecture according to a generic algorithm;
• successively training and evaluating the neural networks represented by the modified symbol strings and modifying the symbol strings representing improved neural networks.

An optimized artificial neural network, comprising:

• a plurality of input neurons for receiving network input signals and for developing output signals in response thereto;
• a plurality of output neurons receptive of signals for developing network output signals;
• a plurality of hidden neurons which receive input signals and develop output signals;
• signal transmission means comprised of a plurality of signal paths for applying output signals from said input neurons to said hidden neurons and for applying output signals from said hidden neurons to said output neurons;
• and
• the number of hidden neurons, the neuron threshold functions and the signal path weights having values optimized by supervised learning and network modification by a genetic algorithm.