Proposed a chaotic cellular neural network system, in-depth study of the system dynamic characteristics through theoretical analysis and simulation, to obtain the system Lyapunov exponent spectrum, also designed the hardware circuit, and get the physical implementation of chaos system through the FPGA, observing all kinds of chaotic attractor. The experimental results are fully consistent with the Matlab simulation results, thus this physical implementation is correct and feasible.

A term-ranking method is put forward to optimize the global model composed of radial basis functions to improve the predictability of the model. The effectiveness of the proposed method is examined by numerical simulation and experimental data. Numerical simulations indicate that this method can significantly lengthen the prediction time and decrease the Bayesian information criterion of the model. The application to real voice signal shows that the optimized global model can capture more predictable component in chaos-like voice data and simultaneously reduce the predictable component (periodic pitch) in the residual signal.