A divide and conquer method for learning large Fuzzy Cognitive Maps

Fuzzy Cognitive Maps (FCMs) are a convenient tool for modeling and simulating dynamic systems. FCMs were applied in a large number of diverse areas and have already gained momentum due to their simplicity and easiness of use. However, these models are usually generated manually, and thus they cannot be applied when dealing with large number of variables. In such cases, their development could be significantly affected by the limited knowledge and skills of the designer. In the past few years we have witnessed the development of several methods that support experts in establishing the FCMs or even replace humans by automating the construction of the maps from data. One of the problems of the existing automated methods is their limited scalability, which results in inability to handle large number of variables. The proposed method applies a divide and conquer strategy to speed up a recently proposed genetic optimization of FCMs. We empirically contrast several different designs, includ...

• Type: Journal
• Year: 2010
• Where: FSS
• Authors: Wojciech Stach, Lukasz A. Kurgan, Witold Pedrycz
• Keywords: FCMs, FSS 2010, Fuzzy Cognitive Maps, Large Number