non-linear functional representation of the aerodynamic response provides a convenient mathematical model for motion-induced unsteady transonic aerodynamic loads response, that accounts for both complex non-linearities and time-history effects. a recent development, based on functional approximation theory, has established a novel functional form; namely, the multi-layer functional. for a large class of non-linear dynamic systems, such multi-layer functional representations can be realised via finite impulse response (fir) neural networks. identification of an appropriate fir neural network model is facilitated by means of a supervised training process in which a limited sample of system input-output data sets is presented to the temporal neural network. the present work describes a procedure for the systematic identification of parameterised neural network models of motion-induced unsteady transonic aerodynamic loads response. the training process is based on a conventional genetic algorithm to optimise the network architecture, combined with a simplified random search algorithm to update weight and bias values. application of the scheme to representative transonic aerodynamic loads response data for a bidimensional airfoil executing finite-amplitude motion in transonic flow is used to demonstrate the feasibility of the approach. the approach is shown to furnish a satisfactory generalisation property to different motion histories over a range of mach numbers in the transonic regime.