Universal Learning Network-Based Fuzzy System and Its Application to Non-Linear Control System

抄録

Universal Learning Network (ULN) which can be used to model and control the large-scale complicated systems in the network framework has been presented. In this paper, a method to construct the fuzzy model with a multi-dimension input membership function using ULN is presented. The fuzzy model under the framework of ULN is called Universal Learning Network-based Fuzzy Inference System (ULNFIS), which possesses certain advantages over other networks such as neural networks. It is also introduced how to imitate a real system with ULN and how to construct a control scheme using ULNFIS. ULN allows any differentiable non-linear functions as node functions. And simulations are carried out in order to compare the performance of the fuzzy control using ULNFIS with the neural network control. It is shown that the fuzzy control has better performance for the generalization capability than the neural network control.