Drift model identification methods for inertial platform based on Elman network

The characteristic of the drift error of inertial platform is a high-order nonlinear dynamic system using the neural networks' abilities of universal approximation of differentiable trajectory and capturing system dynamic information, the drift error identifying project of inertial platform is presented based on Elman networks structure. First, the drift error model of inertial platform is established, and after selecting the input and output for network, momentum and alterable speed algorithm is used to speed up the network convergence. On the basis of the algorithm, the extended nonlinear node function in the hidden network not only improves the learning speed of network, but also satisfies the need of accuracy on system identification. Through the drift error data measured on inertial platform the training shows that the scheme achieves a relatively satisfying identification result.