M-CFIS-R: Mamdani Complex Fuzzy Inference System with Rule Reduction Using Complex Fuzzy Measures in Granular Computing

Abstract

Complex fuzzy theory has strong practical background in many important applications, especially in decision-making support systems. Recently, the Mamdani Complex Fuzzy Inference System (M-CFIS) has been introduced as an effective tool for handling events that are not restricted to only values of a given time point but also include all values within certain time intervals (i.e., the phase term). In such decision-making problems, the complex fuzzy theory allows us to observe both the amplitude and phase values of an event, thus resulting in better performance. However, one of the limitations of the existing M-CFIS is the rule base that may be redundant to a specific dataset. In order to handle the problem, we propose a new Mamdani Complex Fuzzy Inference System with Rule Reduction Using Complex Fuzzy Measures in Granular Computing called M-CFIS-R. Several fuzzy similarity measures such as Complex Fuzzy Cosine Similarity Measure (CFCSM), Complex Fuzzy Dice Similarity Measure (CFDSM), and Complex Fuzzy Jaccard Similarity Measure (CFJSM) together with their weighted versions are proposed. Those measures are integrated into the M-CFIS-R system by the idea of granular computing such that only important and dominant rules are being kept in the system. The difference and advantage of M-CFIS-R against M-CFIS is the usage of the training process in which the rule base is repeatedly changed toward the original base set until the performance is better. By doing so, the new rule base in M-CFIS-R would improve the performance of the whole system. Experiments on various decision-making datasets demonstrate that the proposed M-CFIS-R performs better than M-CFIS.

Chẩn đoán bệnh trong y học cổ truyền: Hướng tiếp cận dựa trên đồ thị tri thức mờ dạng cặp

“…Để áp dụng một hệ suy diễn mờ (Fuzzy Inference System) [9,[12][13][14], một số điều kiện sau cần được thỏa mãn: Thứ nhất, một thang đánh giá rõ ràng về các tham số của mỗi hệ suy diễn mờ phải được thống nhất. Thứ hai, các thuật ngữ về mức độ của các tham số này phải được đưa ra.…”

The Novel CFRG -Based Complex Fuzzy Transfer Learning System

“…They need to be more comprehensive to account for periodic information and contain exact but ambiguous factors, which causes information loss. To cope with periodic elements in data, the Mamdani complex fuzzy inference system (M-CFIS) [15] was recently introduced with a specific inference structure and some extensions such as [7,18].…”

Adaptive Spatial Complex Fuzzy Inference Systems With Complex Fuzzy Measures

“…According to the series of research on complex fuzzy systems, in 2019, Selvachandran et al introduced the Mamdani complex fuzzy inference system (M-CFIS) [26] as a valuable tool for addressing problems with vague and temporal factors. Subsequently, the author proposed two enhanced versions of M-CFIS [27], [28]. The first version improved the rulebases by leveraging complex fuzzy measurements and granular computing, while the second version decreased the computational time by employing fuzzy knowledge graphs.…”