| 一种模糊认知图分类器构造方法 |
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| 作者姓名： | 马楠 杨炳儒 翟云 李广原 张德政 |
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| 作者单位： | 1. 北京科技大学计算机与通信工程学院, 北京 100083; |
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| 基金项目： | 国家自然科学基金;国家重点基础研究发展计划（973计划）;北京市市属市管高等学校人才强教计划 |
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| 摘 要： | 提出了一种新的模糊认知图分类器模型构造方法,它包括构建流程、激活函数、推理规则和学习方法等核心构件.模型利用提出的动态交叉变异算子自适应遗传进化过程,实现种群间自动调节和自动适应.仿真实验表明:本文提出的模型增强了局部随机搜索能力,加强了算法的全局收敛能力,与其他经典分类方法相比,不但性能较好,而且具有较强的抗噪能力,从而具有更强的鲁棒性.
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| 关 键 词： | 模糊认知图 分类器 分类 模糊集合论 学习算法 |
| 收稿时间： | 2011-07-05 |
Classifier construction based on the fuzzy cognitive map |
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| Affiliation: | 1. School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China;2. College of Information Technology, Beijing Union University, Beijing 100101, China;3. College of Computer Science, Liaocheng University, Liaocheng 252000, China |
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| Abstract: | A novel construction method of classifier models based on the fuzzy cognitive map was proposed,which consists of model structure,activation functions,inference rules and learning algorithms.The model employs dynamically self-adaptive crossover and mutation operators to automatically adjust the evolution process within populations.Simulation experiments prove that the model enhances the capabilities of local random search and global convergence.Compared with other classical classification algorithms,the model not only shows a better classification performance,but also has powerful noise-immune ability which renders it robust. |
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