计算机应用 ›› 2012, Vol. 32 ›› Issue (11): 3044-3046.DOI: 10.3724/SP.J.1087.2012.03044

• 人工智能 • 上一篇    下一篇

基于预测状态表示的多变量概率系统预测

汪庆淼1,2,鞠时光1,2   

  1. 1. 江苏大学 计算机科学与通信工程学院, 江苏 镇江 212013
    2. 苏州大学 计算机科学与技术学院,江苏 苏州 215006
  • 收稿日期:2012-05-22 修回日期:2012-06-28 发布日期:2012-11-12 出版日期:2012-11-01
  • 通讯作者: 汪庆淼
  • 作者简介:汪庆淼(1977-),男,浙江绍兴人,实验师,博士研究生,主要研究方向:模式识别、系统建模;鞠世光(1955-),男,江苏南通人,教授,博士生导师,主要研究方向:信息安全、模式识别、空间数据库管理。
  • 基金资助:
    国家自然科学基金资助项目(11102124)

Prediction of multivariate probabilistic systems based on predictive state representation

WANG Qing-miao1,2,JU Shi-guang1,2   

  1. 1. School of Computer Science and Technology,Soochow University, Suzhou Jiangsu 215006,China
    2. School of Computer Science and Telecommunication Engineering, Jiangsu University, Zhenjiang Jiangsu 212013,China
  • Received:2012-05-22 Revised:2012-06-28 Online:2012-11-12 Published:2012-11-01
  • Contact: WANG Qing-miao

摘要: 针对由于多变量概率系统预测高复杂度而导致的建模困难问题,提出了一种基于预测状态表示(PSR)的系统建模新方法,首先介绍一种通用多变量过程概念,并进一步用此概念描述多变量系统。在此基础上,引入了针对多变量系统的预测模型MVPSR,模型基于可观测信息,可在有限维实现对多变量的预测。实验结果表明,该近似模型有效降低了系统预测的复杂度。

关键词: 多变量, 预测状态表示, 通用随机过程, 可观测信息, 核查询

Abstract: In this paper, a new method based on Predictive State Representation (PSR) was proposed to solve high complexity of multivariate probability system. The paper introduced a new concept about general multivariate process in the first, and then described the multivariate system with the concept. Furthermore, the authors imported MultiVariate (MV)PSR as prediction model for multivariate system. The model was based on observable information and could realize the multivariate prediction in the finite dimensions. The experimental result shows that the approximate model effectively reduces the complexity of the system prediction.

Key words: multivariate, Predictive State Representation (PSR), common random process, observable information, core query

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