Benaissa, B.; Kobayashi, M.; Kinoshita, K.; Takenouchi, H. A Novel Approach for Individual Design Perception Based on Fuzzy Inference System Training with YUKI Algorithm. Axioms2023, 12, 904.
Benaissa, B.; Kobayashi, M.; Kinoshita, K.; Takenouchi, H. A Novel Approach for Individual Design Perception Based on Fuzzy Inference System Training with YUKI Algorithm. Axioms 2023, 12, 904.
Benaissa, B.; Kobayashi, M.; Kinoshita, K.; Takenouchi, H. A Novel Approach for Individual Design Perception Based on Fuzzy Inference System Training with YUKI Algorithm. Axioms2023, 12, 904.
Benaissa, B.; Kobayashi, M.; Kinoshita, K.; Takenouchi, H. A Novel Approach for Individual Design Perception Based on Fuzzy Inference System Training with YUKI Algorithm. Axioms 2023, 12, 904.
Abstract
This paper presents a novel approach for individual design perception modeling using the YUKI-trained Fuzzy Inference System. The study focuses on understanding how individuals perceive design based on personality traits, particularly openness to experience. The proposed YUKI algorithm optimizes the FCM clustering algorithm, enhancing its ability to handle uncertain and imprecise data. The YUKI-trained FIS generates several Sugeno-type FIS models to predict design perception, to minimize the Root Mean Squared Error between the model prediction and the actual design perception of participants. The results demonstrate that the YUKI-trained FIS offers more accurate predictions compared to the traditional FCM-trained FIS, and the RMSE values for individual design perceptions fall within a satisfactory range of 0.84 to 1.32. The YUKI-trained FIS proves effective in clustering individuals based on their level of openness, providing insights into how personality traits influence design perception.
Computer Science and Mathematics, Information Systems
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
