权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Studies on Parameter Identification Using Fuzzy Random Data

利用模糊随机数据进行参数辨识的研究

基本信息

批准号：
08680473
负责人：
FUKUDA Tokuo
金额：
$ 0.9万
依托单位：
OTEMON GAKUIN UNIVERSITY
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
1996
资助国家：
日本
起止时间：
1996 至 1998
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-08680473/
关键词：
Fuzzy Random Vectors Set Representations of Fuzzy Sets Multi-valued Logic Theory of Correspondences Statistical Moments 曖昧不規則データ曖昧推定量大数の法則 Hausdorff距離多値理論対応の理論

项目摘要

In this research, a class of fuzzy random vectors(FRVCs) and their statistical moments have been introduced from the consistent viewpoint of vague perceptions of random phenomena, and some properties of FRVCs and their statistical moments up to second ones have been examined theoretically.The features of proposed FRVCs are as follows :1. Set representations of fuzzy sets are thoroughly adopted because of ilie feasibility for describing operations between FRVCs.2. By adopting the family of compact convex correspondences as the set representation of a FRVCS, we have been able to introduce the reasonable definition of expectations of FRVCs, which is basically compatible with (scalar) fuzzy random variables proposed by Kwakernaak and is never obtained from their simple extensions.3. The proposed concept of FRVCs as vague perceptions of random vectors may be, in some sense, a bridge between two major types of flizzy mndoin variables.Furthermore, the following items should be investigated in the future :1. Definitions of higher statistical moments of FRVCs and their estimators.2. Statistical properties of FRVCS under more moderate conditions3. Applications of the proposed approach to fuzzy random data obtained by vague perceptions of output processes of time series described by AR, MA and ARMA models and so on.4. Identification of system parameters of time series models as mentioned above bv using fuzzy random data.

本文从随机现象模糊感知的观点出发，引入了一类模糊随机向量及其统计矩，并从理论上考察了一类模糊随机向量及其统计矩的一些性质。建议的frvc的特点如下：由于模糊集的集合表示在描述frvc之间的操作上不太可行，因此被彻底采用。通过采用紧致凸对应族作为FRVCS的集合表示，我们能够引入FRVCS期望的合理定义，该定义与Kwakernaak提出的（标量）模糊随机变量基本相容，并且永远不能从它们的简单推广中得到。从某种意义上说，frvc作为随机向量的模糊感知的概念可能是两种主要类型的模糊变量之间的桥梁。此外，未来还应调查以下几个项目：1。frvc的高统计矩及其估计量的定义。中等条件下FRVCS的统计特性本文提出的方法在由AR、MA和ARMA等模型描述的时间序列输出过程的模糊感知获得的模糊随机数据中的应用。利用模糊随机数据辨识上述时间序列模型的系统参数。