A Fast Structural Optimization Technique for IDS Modeling

Abstract

The ink drop spread (IDS) method is a modeling technique that is proposed as a new paradigm of soft computing. In this method, the structure of models is determined by the partitioning of the input domain. In order to obtain a high-accuracy model, it is necessary to determine the optimal number of partitions, i.e., structural optimization must be performed. This paper proposes a structural optimization technique for IDS modeling. The IDS model comprises multiple processing units, each of which is a modeling engine that develops a feature of the target system in the form of an easily comprehensible image on a two-dimensional plane. The proposed technique performs structural optimization with a small number of searches by analyzing the image information generated in the processing units instead of evaluating the model error using validation data.