Universal learning network-based fuzzy system and its application to nonlinear control system

The 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 fuzzy model with multidimension 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. Simulations are carried out in order to compare the performance of fuzzy control and neural network control by using ULNFIS. It has been shown that the fuzzy control has better performance for generalization capability than the neural network control.