Parameter estimation based on convolutional neural network and state sequence

To reduce the time cost in parameter estimation processes, a new parameter estimation scheme based on a Convolutional Neural Network is proposed. This scheme can quickly estimate multiple parameters based on a temporal sequence of system states by avoiding time-consuming iterations of numerical models. At the same time, a two-way standardization method is used to help the neural network extract features better. The scheme has been tested in a Lorenz63 nonlinear system. Results show that it can effectively estimate model parameters corresponding to the current physical state with a calculation time only 4% of the particle swarm optimization method.