Comparison of fuzzy rule-based learning and inference systems
Krisztián Balázs, László Tamás Kóczy, János Botzheim · 2008
In our work we have compared various fuzzy rule based learning and inference systems. The base of the investigations was a modular system that we have implemented in C language. It contains several alternative versions of the two key elements of rule based learning – namely, the optimization algorithm and the inference method – which can be found in the literature. We obtained very different properties when combining these alternatives (changing the modules and connecting them) in all possible ways. The investigations determined the values of the quality measures (complexity and accuracy) of the obtained alternatives both analitically and experimentally where it was possible. Based on these quality measures the combinations have been ordered according to different aspects.