System and method of global optimization using artificial neural networks

摘要

A method of global optimization of complex, highly nonlinear, multivariant systems is described. An artificial neural network (ANN) is trained to create an approximate inverse model. The desired behavior for a particular system is then input to the inverse model to derive approximate model parameters for the particular system. Optimization of the approximate model parameters yields optimal model parameters. The method is applied to the synthesis of mechanical linkages where examples of a type of linkage mechanism are used to train an ANN and derive the approximate inverse model. Inverse models for a number of linkage mechanism types are derived and stored. For a linkage mechanism with unknown linkage parameters, a power spectrum representation of the coupler curve is developed and the inverse model for the type of linkage mechanism retrieved. The representation of the desired coupler curve is input and the approximate linkage parameters derived. Optimization further refines the linkage parameters.