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A multi-stage learning-based fuzzy cognitive maps for tobacco use

NEURAL COMPUTING & APPLICATIONS 2020年第18期32卷 15101-15118页

作者： Ciftci, Pinar Kocabey Durmusoglu, Zeynep Didem Unutmaz Gaziantep Univ Dept Ind Engn TR-27310 Sehitkamil Gaziantep Turkey

Fuzzy cognitive map (FCM) is an important approach for modeling the behavior of dynamic systems. FCM's ability to represent casual relationships between the concepts (factors, attributes, etc.) has attracted the interest of researchers from different disciplines. The construction process of FCMs is mostly initialized with expert knowledge because FCMs can conveniently incorporate available information and expertise in the determination of vital parameters and relations of the system. However, their higher dependence on expert knowledge may significantly influence the reliability of the model due to the increase in subjectivity. In order to avoid weaknesses depending on expert knowledge, learning algorithms that search for the appropriate relationships between the concepts have been used with FCM studies. In this paper, a FCM analysis was performed for tobacco use to understand the cause-effect relationships between demographic characteristics of people (such as gender, age range, and residence type) and likelihood to tobacco use. In order to reduce the impact of external interventions (from experts), a multi-stage learning procedure was applied by integrating two different learning algorithms (nonlinear hebbian learning algorithm and extended Great Deluge algorithm). The results showed that the multi-stage learning procedure increased the accuracy of the model and provided more reliable maps for the studied system. They also proved that the multi-stage learning procedures can help to reduce the dependency to expert knowledge and improve the robustness of the study.

关键词： Fuzzy cognitive map nonlinear hebbian learning algorithm Extended Great Deluge algorithm

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An Integrated Framework for learning Fuzzy Cognitive Map using RCGA and NHL algorithm

An Integrated Framework for Learning Fuzzy Cognitive Map usi...

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4th International Conference on Wireless Communications, Networking and Mobile Computing

作者： Zhu Yanchun Zhang Wei Beijing Normal Univ Sch Econ & Business Adm Beijing 100875 Peoples R China Ctrl Univ Finance Econ Sch Informat Beijing 100081 Peoples R China

ISBN: (纸本)9781424421077

The methodology of developing Fuzzy Cognitive Map (FCM) still exhibited weaknesses. This paper investigated a hybrid framework for learning FCM, which was combined of the real-coded genetic (RCGA) algorithm and nonlinear hebbian learning (NHL) algorithm. This approach combined the synergistic theories of neural networks and fuzzy logic. The hybrid algorithm is introduced, presented and applied successfully in real-world problems of partner selection.

关键词： Fuzzy Cognitive Map nonlinear hebbian learning algorithm real-coded genetic algorithm

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