| 基于严格二叉树编码和GA的模糊树结构学习 |
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| 引用本文： | 刘长良,王梓齐.基于严格二叉树编码和GA的模糊树结构学习[J].系统仿真学报,2020,32(8):1473-1480. |
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| 作者姓名： | 刘长良 王梓齐 |
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| 作者单位： | 1.华北电力大学 新能源电力系统国家重点实验室,北京 102206;2.华北电力大学 控制与计算机工程学院,河北 保定 071003 |
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| 基金项目： | 北京市自然科学基金(4182061),中央高校基本科研业务费专项资金(2018ZD05) |
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| 摘 要： | 为解决模糊树模型结构学习中存在的信息冗余、寻优效率低等问题,提出了一种基于严格二叉树编码和遗传算法的结构学习方法。通过严格二叉树编码对模型结构进行编码,改善了现有矩阵编码的信息冗余问题;考虑到编码特殊性和算法收敛性,提出了一种改进的遗传算法用来对模糊树模型的结构进行寻优。实验结果表明,不同数据集上该算法的稳定性和计算速度均较好,能够寻找到一个较优的二叉树结构,从而提高模糊树模型的建模精度。
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| 关 键 词： | 模糊树 严格二叉树编码 遗传算法 结构学习 |
| 收稿时间： | 2019-01-11 |
Structure Learning of Fuzzy-tree Based on Rigorous Binary Tree Code and Genetic Algorithm |
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| Liu Changliang,Wang Ziqi.Structure Learning of Fuzzy-tree Based on Rigorous Binary Tree Code and Genetic Algorithm[J].Journal of System Simulation,2020,32(8):1473-1480. |
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| Authors: | Liu Changliang Wang Ziqi |
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| Institution: | 1. State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China;2. School of Control and Computer Engineering, North China Electric Power University, Baoding 071003, China |
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| Abstract: | To solve the problems of information redundancy and low optimization efficiency in the structure learning of fuzzy-tree model, a method based on rigorous binary tree code and genetic algorithm is proposed. The structure of fuzzy-tree model is coded by rigorous binary tree code, which improves the information redundancy of the existing matrix code. Considering the particularity of the code and the convergence of the algorithm, an improved genetic algorithm is proposed to optimize the structure of fuzzy-tree model. The experimental results show that the algorithm has good stability and computing speed on different data sets, and can find a better binary tree structure, and that improves the modeling accuracy of fuzzy-tree model. |
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| Keywords: | fuzzy-tree rigorous binary tree code genetic algorithm structure learning |
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