Performance of the IDS Method as a Soft Computing Tool

Performance factors such as robustness, speed, and tractability are important for the realization of practical computing systems. The aim of soft computing is to achieve these factors in practice by tolerating imprecision and uncertainty instead of depending on exact mathematical computations. The ink drop spread (IDS) method is a modeling technique that has been proposed as a new approach to soft computing. This method is characterized by a modeling process that uses image information without including complex formulas. In this study, the performance of the IDS method is investigated in terms of robustness, speed, and tractability, which are typical criteria that determine the importance of soft computing tools. Robustness is evaluated on the basis of noise tolerance and fault tolerance. Tractability is discussed from the viewpoints of interpretability and transparency. Based on comparative evaluations with artificial neural networks and fuzzy inference systems, this study demonstrates that the IDS method has superior capability to function as a soft computing tool.