A Model for Hidden Behavior Prediction of Complex Systems Based on Belief Rule Base and Power Set

Zhi Jie Zhou, Guan Yu Hu, Bang Cheng Zhang, Chang Hua Hu, Zhi Guo Zhou, Pei Li Qiao

Research output: Contribution to journalArticle

15 Scopus citations

Abstract

It is important to predict the hidden behavior of a complex system. In the existing models for predicting the hidden behavior, the hidden belief rule base (HBRB) is an effective model which can use qualitative knowledge and quantitative data. However, the frame of discernment (FoD) of HBRB which is composed of some states or propositions and the universal set including all states or propositions is not complete. The global ignorance and local ignorance cannot be considered at the same time, which may lead to the inaccurate forecasting results. To solve the problems, a new HBRB model named as PHBRB in which the hidden behavior is described on the FoD of the power set is proposed in this correspondence paper. Furthermore, by using the evidential reasoning rule as the inference tool of PHBRB, a new projection covariance matrix adaption evolution strategy is developed to optimize the parameters of PHBRB so that more accurate prediction results can be obtained. A case study of network security situation prediction is conducted to demonstrate the effectiveness of the newly proposed method.

Original languageEnglish (US)
JournalIEEE Transactions on Systems, Man, and Cybernetics: Systems
DOIs
StateAccepted/In press - Aug 18 2017

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Keywords

  • Belief rule base (BRB)
  • Communication networks
  • covariance matrix adaption evolution strategy (CMA-ES)
  • evidential reasoning (ER) rule
  • Forecasting
  • hidden behavior prediction
  • Hidden Markov models
  • Inference algorithms
  • power set.
  • Predictive models
  • Security
  • Uncertainty

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Information Systems
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering

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