chaotic time series prediction using rough-neural networks

‎artificial neural networks with amazing properties‎, ‎such as universal approximation‎, ‎have been utilized to approximate the nonlinear processes in many fields of applied sciences‎. ‎this work proposes the rough-neural networks (r-nns) for the one-step ahead prediction of chaotic time series‎. ‎we adjust the parameters of r-nns using a continuous-time lyapunov-based training algorithm‎, ‎and prove its stability using the continuous form of lyapunov stability theory‎. ‎then‎, ‎we utilize the r-nns to predict the well-known mackey-glass time series‎, ‎and henon map‎, ‎and compare the simulation results with some well-known neural models‎.