Learning of Fuzzy Cognitive Maps With Varying Densities Using A Multiobjective Evolutionary Algorithm

Fuzzy cognitive maps (FCMs) are cognition fuzzy influence graphs, which are based on fuzzy logic and neural networks. Many automated learning algorithms have been proposed to reconstruct FCMs from data, but most learned maps using such methods are much denser than those constructed by human experts....

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Bibliographic Details
Published in:IEEE transactions on fuzzy systems Vol. 24; no. 1; pp. 71 - 81
Main Authors: Chi, Yaxiong, Liu, Jing
Format: Journal Article
Language:English
Published: New York IEEE 01.02.2016
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Analytical models
Artificial neural networks
Biological cells
Densities
Evolutionary computation
Fuzzy cognitive maps
Multi-objective evolutionary algorithms
Multi-objective optimization problems
Neural networks
Optimization
Sociology
Statistics
fuzzy cognitive maps
Densities
multiobjective evolutionary algorithms (MOEAs)
multiobjective optimization problems (MOP)
ISSN:1063-6706, 1941-0034
Online Access:Get full text
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Summary:Fuzzy cognitive maps (FCMs) are cognition fuzzy influence graphs, which are based on fuzzy logic and neural networks. Many automated learning algorithms have been proposed to reconstruct FCMs from data, but most learned maps using such methods are much denser than those constructed by human experts. To this end, we first model the FCM learning problem as a multiobjective optimization problem, and then propose a multiobjective evolutionary algorithm, labeled as MOEA-FCM, to learn FCM models. MOEA-FCM is able to learn FCMs with varying densities at the same time from input historical data, which can provide candidate solutions with different properties for decision makers. In the experiments, the performance of MOEA-FCM is validated on both synthetic and real data with varying sizes and densities. The experimental results demonstrate the efficiency of MOEA-FCM and show that MOEA-FCM can not only reconstruct FCMs with high accuracy without expert knowledge, but also create a diverse Pareto optimal front which consists of FCMs with varying densities. The significance of this study is that decision makers can choose different FCM models provided by MOEA-FCM based on their practical requirements.
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ISSN:1063-6706
1941-0034
DOI:10.1109/TFUZZ.2015.2426314