Hierarchical control system based on unsupervised fuzzy-neuro system

Recently, fuzzy systems are applied to various systems. However, lack of learning ability, the determination of most fuzzy rules and membership function was made by human experts. In this paper, the authors propose a hierarchical control system based on the unsupervised RBF fuzzy system. The learning algorithm of the fuzzy system is based on genetic algorithms. This hierarchical control system has the skill database, which manages the fuzzy controllers acquired through the unsupervised learning process. Thus, the proposed system can use the acquired fuzzy controller effectively and it leads to reduce the iteration time for a new object. The effectiveness of the proposed method is shown through the simulations of the cart-pole problem