Knowing the equation of an unknown dynamical system is essential when trying to apply optimal control. Sometimes researchers do not have a comprehensive knowledge to a nonlinear system. The unknown part might be the function representing the relation between states (e.g. transfer function), or key parameters of a dynamical system (e.g. proportional constant of spring in a linear spring system). Various methods have been developed to identify the dynamics of an unknown system. In this thesis, multiple approaches include Neural Network polynomial Extraction (NN-poly), Sparse Identification of nonlinear Dynamics (SINDy) and Non-Uniform Discrete Fourier Transform (NUDFT) are compared over their ability to find the expression of unknown systems or to estimate key parameters of a dynamical system. Multiple tasks with different purposes are created to test the performances of these methods.