Fuzzy inference provides a way to describe system behavior using humanly understandable rules. Based on this advantage, fuzzy inference has been applied in a wide variety of fields, including control, prediction, and pattern recognition. It has also had a corresponding impact on industrial applications. The four articles included in this special issue cover the advances made in fuzzy inference and related techniques. The first paper proposes a method for fuzzy rule interpolation on the basis of the generalized mean. This method makes it possible to perform nonlinear mapping of convex fuzzy sets even with sparse fuzzy rules. The second paper proposes a fuzzy clustering algorithm, landmark fuzzy neighborhood DBSCAN (landmark FN-DBSCAN). This algorithm is quite efficient in the clustering of large data sets, particularly compared to conventional density-based algorithms. Fuzzy clustering can be used to construct fuzzy rule bases. The third paper applies fuzzy inference to ultrasonic human brain imaging based on YURAGI synthesis. In this method, the thickness of bones is calculated effectively from synthesized waves using fuzzy inference. The fourth paper applies adaptive neurofuzzy inference systems (ANFIS) to financial institution failure prediction. These systems function effectively in the handling of the finance data of Thai firms with high nonlinearity. As a guest editor, I really appreciate the efforts of the contributors and reviewers. I am very grateful to staffs in JACIII editorial office for their kind support.
Fuzzy Inference System for Data Processing in Industrial Applications
