A fuzzy-nets training scheme for controlling nonlinear systems

A fuzzy-nets system has been developed to create fuzzy rule banks and to control nonlinear systems. The training procedure includes five steps. First, fuzzy regions of input and output spaces are defined based on the boundaries of the system. The second step is to generate fuzzy rules by given data sets which are feedback data from the system. Then, conflicting rules are resolved through bottom-up and top-down methodologies. In the fourth stage the rules are combined to generate a fuzzy rule base. Finally, an appropriate defuzzification methodology is defined for controlling the systems. To test the system, experimental data for a backing up a truck were collected and trained through the training scheme. An optimal fuzzy rule bank was then developed and various tests were performed and evaluated. The simulation results show that the scheme is able to produce an appropriate rule bank for controlling a nonlinear system.