A New Deep Neural Network Based Dynamic Fuzzy Cognitive Map Weight Updating Approach

Abstract

Fuzzy cognitive maps are a soft computational technique with artificial neural network nature expressed by graphs used to model complex systems. Fuzzy cognitive maps are used in areas like health, business, energy, computer science, etc. in the literature. It is used to solve problems in many areas. Fuzzy cognitive maps have a non-dynamic method of weight determination and updating in their classical applications. This is usually done by subjecting the expert opinions to fuzzy membership functions. In some studies, the weights obtained by using expert data are improved by utilizing historical data of the system modeled. In some studies, weight determination and updating process is realized by using intuitive optimization methods without benefiting from expert opinions. Here, fuzzy cognitive maps using a different weight matrix for different input values are considered dynamic. In this study, we propose a dynamic fuzzy cognitive map structure that performs the weight update process with deep learning. Here, the cognitive map weights determined in the data set used for deep neural network training are determined by a genetic optimization-based method using past system data. The proposed new DFCM structure has been tested on two different scenarios. In addition, the performance comparison with deep artificial neural network models dealing with the same scenarios was performed. When the experimental and comparative results are examined, the performance of the proposed method is quite satisfactory.