A novel fuzzy-type zeroing neural network for dynamic matrix solving and its applications

Solving dynamic matrix problems has always been an important research topic in the field of science and engineering, for this, we design a method to solve time-varying problems through a novel fuzzy-type zeroing neural network (NFTZNN) model. A new activation function is proposed to construct zeroing neural network model and ensure fast convergence in predefined-time. In addition, combined with the fuzzy control theory, convergence parameters are replaced by fuzzy parameters to enhance the adaptability of the model as well as its robustness in the presence of external noise perturbations. Furthermore, the convergence and robustness of this neural network system are analyzed theoretically, ensuring the effectiveness. Finally, the feasibility of the model is further verified by the simulation experiments including matrix inversion, circuit solution, encryption transmission and dual-arm robot control.