NHL and RCGA Based Multi-Relational Fuzzy Cognitive Map Modeling for Complex Systems

Abstract

In order to model multi-dimensions and multi-granularities oriented complex systems, this paper firstly proposes a kind of multi-relational Fuzzy Cognitive Map (FCM) to simulate the multi-relational system and its auto construct algorithm integrating Nonlinear Hebbian Learning (NHL) and Real Code Genetic Algorithm (RCGA). The multi-relational FCM fits to model the complex system with multi-dimensions and multi-granularities. The auto construct algorithm can learn the multi-relational FCM from multi-relational data resources to eliminate human intervention. The Multi-Relational Data Mining (MRDM) algorithm integrates multi-instance oriented NHL and RCGA of FCM. NHL is extended to mine the causal relationships between coarse-granularity concept and its fined-granularity concepts driven by multi-instances in the multi-relational system. RCGA is used to establish high-quality high-level FCM driven by data. The multi-relational FCM and the integrating algorithm have been applied in complex system of Mutagenesis. The experiment demonstrates not only that they get better classification accuracy, but it also shows the causal relationships among the concepts of the system.

Keywords

• fuzzy cognitive map (FCM)
• multi-relational data mining (MRDM)
• nonlinear Hebbian learning (NHL)
• real code genetic algorithm (RCGA)
• complex system