Learning fuzzy cognitive maps using decomposed parallel ant colony algorithm and gradient descent
Learning fuzzy cognitive maps using decomposed parallel ant colony algorithm and gradient descent
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DOI：
10.1109/fskd.2015.7381919
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发表时间：
2015-08
期刊：
影响因子：
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通讯作者：
Nan Ye;Rongwei Zhang;Kena Yu;Dehong Wang
中科院分区：
文献类型：
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作者：
Nan Ye;Rongwei Zhang;Kena Yu;Dehong Wang
模糊认知地图（FCM）是一种因果建模和因果推理的模型。它用模糊变量来表示真实世界的概念以及概念之间的因果关系。模糊变量的主要好处是模型对观测数据中的误差更鲁棒。虽然FCM已经在不同的研究领域得到了广泛的应用，但是如何有效地构造大规模的FCM模型仍然是一个有待解决的问题。为了进一步提高现有FCM学习算法的效率，提出了一种结合蚁群优化算法、梯度下降局部搜索和分解并行计算框架的新算法，用于从观测数据中构建大规模FCM。一组网络推理问题被用来评估所提出的算法的性能，并与其他算法，包括传统的蚁群优化，和真实的编码遗传算法的结果进行了比较。实验结果表明，我们的算法优于其他算法的模型精度。我们还比较了非并行蚁群优化算法和提出的并行算法所需的计算时间。当节点数合适时，加速比可以非常接近线性加速比。
Fuzzy cognitive maps (FCMs) are a model for causal modeling and causal inference. It represents the real-world concepts and the causal relations between the concepts by using fuzzy variables. The major benefit of the fuzzy variables is that the model is more robust to the errors in the observed data. Although FCMs have been widely used in different research areas, it is still an open problem to efficiently construct large scale FCM models. To further improve the efficiency of the existing FCM learning algorithms, we propose a new algorithm that combines ant colony optimization algorithm, gradient descent local search and a decomposed parallel computing framework to build large scale FCMs from observational data. A set of network inference problem is used to evaluate the performance of the proposed algorithm and the results are compared to other algorithms including traditional ant colony optimization, and real coded genetic algorithms. Experimental results suggest that our algorithm outperforms the other algorithms in terms of model accuracy. We also compared the computation time required by the non-parallel ant colony optimization algorithm and the proposed parallel algorithm. When the number of nodes is appropriate, the speedup could be very close to linear speedup.