An adaptive wavelet differential neural networks based identifier and its stability analysis

In this paper, identification problem of a general class of nonlinear dynamic systems is fully considered using adaptive wavelet differential neural networks. In these networks, the activation functions are described by wavelets where parameters are tuned adaptively. The stability analysis of such identifiers is performed by means of Lyapunov analysis. Asymptotic convergence of the error and boundedness of the parameters are proven. To validate the approach, the neuro-identifier is applied to both the Van der pole oscillator and the twin-tanks plant. The simulation results show that the proposed neuro-identifier outperforms the sigmoid based differential neural network identifier.