System and method of global optimization using artificial neural networks

A method of global optimization comprising the steps of:

• a. developing an approximate inverse model of a system using an artificial neural network (ANN), including the substeps
• constructing a forward model having inputs and outputs with model parameters as inputs and behaviors as outputs,
• building a set of training examples consisting of a set of model parameters and their corresponding behaviors as derived from the forward model,
• developing an ANN with said set of training examples to build an inverse model, said inverse model having inputs and outputs with behaviors as inputs and model parameters as outputs
• storing said inverse model;
• b. determining the approximate model parameters of a system given a desired behavior of the system as inputs including the substeps
• retrieving said inverse model, computing the approximate model parameters from said inverse model;
• and
• c. obtaining optimal model parameters for said system given the approximate model parameters including the substeps
• initializing a local optimization process with the approximate model parameters,
• optimizing said approximate model parameters using the forward model and the optimization process resulting in the optimal model parameters.