Belief Rule Base

Smart risk assessment systems are becoming more and more important in the society. If the chances of reducing and managing certain risks are increased, the impacts can be controlled and reduced significantly. This article surveys different belief-rule-based expert systems and various wireless sensor network technologies that can be used in collaboration to build interesting risk assessment applications. We propose a model for building such an environment and describe a potential application of our proposed model for assessing flood risks in a case study.

Safety assessment is one of important aspects in health management. In safety assessment for practical systems, three problems exist: lack of observation information, high system complexity and environment interference. Belief rule base with attribute reliability (BRB-r) is an expert system that provides a useful way for dealing with these three problems. In BRB-r, once the input information is unreliable, the reliability of belief rule is influenced, which further influences the accuracy of its output belief degree. On the other hand, when many system characteristics exist, the belief rule combination will explode in BRB-r, and the BRB-r based safety assessment model becomes too complicated to be applied. Thus, in this paper, to balance the complexity and accuracy of the safety assessment model, a new safety assessment model based on BRB-r with considering belief rule reliability is developed for the first time. In the developed model, a new calculation method of the belief rule reliability is proposed with considering both attribute reliability and global ignorance. Moreover, to reduce the influence of uncertainty of expert knowledge, an optimization model for the developed safety assessment model is constructed. A case study of safety assessment of liquefied natural gas (LNG) storage tank is conducted to illustrate the effectiveness of the new developed model.

Belief  rule-based  inference  system  introduces  a  belief  distribution  structure  into the conventional  rule-based  system,  which  can  effectively  synthesize  incomplete  and  fuzzy information.  In  order  to  optimize  reasoning  efficiency  and  reduce  redundant  rules,  this  paper proposes a rule reduction method based on regularization. This method controls the distribution of  rules  by  setting  corresponding  regularization  penalties  in  different  learning  steps  and reduces redundant rules. This paper first proposes the use of the Gaussian membership function to  optimize  the  structure  and  activation  process  of the  belief  rule  base,  and  the  corresponding regularization penalty construction method. Then, a step-by-step training method is used to set a  different  objective  function  for  each  step  to  control  the  distribution  of  belief  rules,  and  a reduction  threshold  is  set  according  to  the  distribution  information  of  the  belief  rule  base  to perform rule reduction. Two experiments will be conducted based on the synthetic classification data  set  and  the  benchmark  classification  data  set  to  verify  the  performance  of  the  reduced belief rule base.