Evolutionary computation based system decomposition with neural networks

We present an evolutionary approach to divide a complex control system into smaller sub-systems with the help of neural networks. Thereto, measured channels are partitioned into several disjunct sets, representing possible sub-problems, while the networks are used to assess the quality of the resulting decomposition. We show that this approach is well suited to calculate correct decompositions of complex control systems. Furthermore, the obtained neural networks are used to predict important process factors with considerable better approximation quality than monolithic approaches that have to deal with all input channels in parallel.