A New Partitioning Method for the IDS Method

The ink drop spread (IDS) method is a modeling technique based on the idea of soft computing. This method divides a multi-input-single-output (MISO) target system into multiple single-input-single-output (SISO) systems, and models each SISO system by plotting the input/output data. The IDS method combines the modeling results of SISO systems to model the target. It is important for the IDS method to decide appropriate partitions of the target system in order to accurately model the target. Existing partitioning methods divide each input domain independently of the other inputs, and thus generate unnecessary SISO systems. In this article, we propose a new partitioning method for the IDS method, which divides the input domains by considering the relationship between inputs. We also show that our method can achieve better performance with less partitions than existing methods.