Hong Wang

TL;DR: A recursive optimization solution has been developed and the local stability condition of the closed-loop system has been established and the generality of this algorithm has been proved by the special case study of the minimum variance control for linear Gaussian systems.

TL;DR: An improved estimation algorithm using an adaptive neuro-fuzzy inference system (ANFIS) for unmodeled dynamics is presented and it is confirmed that the nonlinear switching control which adopts the proposed estimation method cannot only guarantee the stability and convergence of the system but can exhibit a desired dynamic performance for the closed-loop system as well.

TL;DR: The proposed novel wavelet neural network modeling method has a higher modeling precision and better generalization ability compared with the conventional WNN modeling based on MSE criteria and has more desirable estimation for modeling error PDF that approximates to a Gaussian distribution whose shape is high and narrow.

TL;DR: A simple linear matrix inequality (LMI) system for the design of static precompensators to reduce the interactions of a multivariable system is proposed and it is shown that its performance is significantly better than previously proposed LMI optimization techniques.

TL;DR: The entropy optimization principle for the stochastic error system is proposed and a real-time optimal FD filter design method is provided and simulations are given to demonstrate the effectiveness of the proposed FD filtering algorithms.