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Development of Unified Learning Paradigm for Fuzzy Pattern Classification Systems

模糊模式分类系统统一学习范式的开发

基本信息

批准号：
12650409
负责人：
ABE Shigeo
金额：
$ 2.3万
依托单位：
Kobe University
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
2000
资助国家：
日本
起止时间：
2000 至 2001
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-12650409/
关键词：
Fuzzy Systems RuleAcquisition Pattern Recognition Function Approximation Neural Networks Generalization Ability

项目摘要

In our previous project we have developed several fuzzy pattern classification systems. In this project, we systemized fuzzy systems and developed unified learning paradigms as follows :1. Development of Multi-dimensional Membership Functions : In the multi-dimensional input space, we developed (truncated) pyramidal membership functions (truncated) polyhedral pyramidal membership functions, bell-shaped membership functions, and analyzed the effect of membership functions on class boundaries.2. Development of Unified Learning Paradigm : By analytically calculating the points where the recognition rate changes when the slopes or locations of the membership function are changed, we calculate the intervals that maximize the recognition rate. Then we tune slopes and/or locations of the membership functions so that they are in those intervals. By this method, the recognition rate of the training data is directly maximized. We evaluated our method for several benchmark data sets.3. Development of a Fuzzy Classifier with Polyhedral Regions : Starting from an initial convex hull, we modify the convex hull adding one datum at a time. Namely, if the datum is in the convex hull, we do nothing. But if it is outside of the convex hull, we modify the convex hull so that the datum is a vertex of the modified convex hull.4. Performance Evaluation: We evaluated the fuzzy systems we have developed and the conventional systems for several benchmark data sets, and demonstrated the effectiveness of our systems over conventional ones.

在我们以前的项目中，我们已经开发了几个模糊模式分类系统。在本计画中，我们将模糊系统系统化，并发展出统一的学习范例如下：1。多维隶属度函数的建立：在多维输入空间中，建立了（截顶）金字塔型隶属度函数、（截顶）多面体金字塔型隶属度函数、钟形隶属度函数，并分析了隶属度函数对类边界的影响.统一学习范式的发展：通过分析计算当隶属函数的斜率或位置改变时识别率变化的点，我们计算出最大化识别率的区间。然后我们调整隶属函数的斜率和/或位置，使它们处于这些区间内。通过该方法，训练数据的识别率直接最大化。我们针对多个基准数据集评估了我们的方法。3.一个多面区域模糊分类器的发展：从一个初始的凸船体开始，我们修改的凸船体增加一个数据的时间。也就是说，如果基准面在凸船体中，我们什么也不做。但如果它在凸船体之外，我们修改凸船体，使基准是修改后的凸船体的顶点.性能评价：我们评估了我们已经开发的模糊系统和传统系统的几个基准数据集，并证明了我们的系统的有效性超过传统的。