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Online Fuzzy Cognitive Map Learning
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DOI：10.1109/TFUZZ.2020.2988845.png)
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摘要
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The fuzzy cognitive map (FCM) is an effective tool for modeling and simulating complex dynamic systems. The research on the problem of learning FCM from the available time series is outstanding. Many batch FCM learning methods have been proposed to address this issue and the performance of these methods is satisfactory. However, these batch-learning methods are difficult to cope with large-scale data sets (for example, the memory in computers is not enough to store all instances) and real-time streaming data, leading to the failure of real-time and online analysis of complex systems. In this article, unlike the existing batch learning methods, such as evolutionary and regression-based methods, we first extend the FCM learning to an online setting, and then develop an effective algorithm based on a follow-the-regularized-leader (FTRL)-proximal style learning algorithm to address the online FCM learning problem, termed as OFCM. The performance of OFCM is validated on constructed benchmark data sets, including synthetic data sets, and gene regulatory network reconstruction data sets. The experimental results demonstrate the merits of OFCM, which can effectively solve online FCM learning problems.
Keyword:
Learning systems
Time series analysis
Real-time systems
Optimization
Genetic algorithms
Memetics
Fuzzy cognitive maps
Big data
data stream
fuzzy cognitive map (FCM)
large-scale optimization
online learning
期刊
IF:
11.9
论文数:
5.0K
被引数:
2.9W
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