Automatic estimation of parameters for the hierarchical reduction of rules of complex fuzzy controllers

The application of fuzzy control to large-scale complex systems is not a trivial task. For such systems the number of the fuzzy IF-THEN rules exponentially explodes. If we have m linguistic properties for each of n variables, we will have m rules combinations of input values. Large-scale systems require special approaches for modelling and control. In our work the system’s hierarchical structure is studied in an attempt to reduce the size of the inference engine for large-scale systems. This method reduces the number of rules considerably. But, in order to do so, the adequate parameters should be estimated, which, in the traditional way, depends on the experience and knowledge of a skilled operator. In this work, we are proposing a method to automatically estimate the corresponding parameters for the hierarchical rule base reduction method to be applied to fuzzy control complex systems. In our approach, the parameters of the hierarchical structure are found through the use of genetic algorithms. The implementation process, the simulation experiments and some results are presented.