A Pareto Optimal Approach to Takagi-Sugeno Fuzzy System Synthesis

Cin-Young Lee and Erik K. Antonsson

Submitted to IEEE Transactions on Fuzzy Systems
(December 2000), IEEE.


Traditionally, the design of fuzzy systems has relied on expert knowledge. Thus, automation of fuzzy system synthesis has been long sought after. An abundance of methods exist for automated fuzzy system design. Particular attention has been given to soft computing approaches such as neural networks and evolutionary computation. However, the majority of these methods either are partially automated (i.e., rules are constructed given the fuzzy partitions) or have a fixed number rules. A novel evolutionary computation approach is developed here to address the aforementioned drawbacks that also has the added benefit of finding a locally Pareto optimal set of solutions. The new approach is tested on two data sets with promising results.