Genetic Algorithm Based Fuzzy Cognitive Map Concept Relationship Determination and Sigmoid Configuration

Abstract

Fuzzy cognitive maps take the features of a problem or a system as a concept to solve a problem or to model real world systems. Here, the positive or negative relationships between the concepts of fuzzy cognitive map are expressed with fuzzy weights, and the proposed solution or cognitive construction of the system model is completed. Previously, many different perspectives have been proposed in the literature for FCM weighting. Some of them utilize an expert group to determine the relationships between concepts, some using Hebbian-based methods, some using population-based meta-heuristic optimization methods, and some FCMs use different supervised computational intelligence methods. For FCMs that use supervised learning techniques in weight determination and updating, it is not possible to provide a data set with system-related conceptual relationships. Data sets for systems or problems generally contain the values that the concept values take over time. In this study, it is aimed to bring the cognitive weights corresponding to the concept values of the raw data sets of the systems to be expressed with FCM by a genetic algorithm based method. The method tested on different scenarios has a very high performance.