Fuzzy complex event processing-based decision-making systems have received considerable research interests recently. In particular, a well-managed operator distribution is required for improving the performance of the fuzzy complex event processing-based decision-making systems. However, the intrinsic uncertainty in dynamic input events increases the difficulties of operator distribution problem. To address these issues, a cost-aware, fault-tolerant and reliable strategy, called CaFtR is proposed for operator scheduling on fuzzy complex event processing systems based on the Technique for Order Preferences by Similarity to an Ideal Solution. The proposed CaFtR method adequately makes use of network resources to achieve continuous and highly available complex event processing regardless of dynamic operator migrations under fuzzy environment. Finally, a case study is provided to illustrate the efficiency of the proposed method, and the utility of our work is demonstrated through an application on the StreamBase system.

CaFtR: A Fuzzy Complex Event Processing Method

Probability transformation of mass function: A weighted network method based on the ordered visibility graph

How to measure the complexity of physiological signals in biological system is an open problem. Various entropy algorithms have been presented, but most of them fail to account for the complexity of time series with high accuracy. In this paper, the concept of Belief Entropy-of-Entropy (BEoE) is introduced, it expands entropy of entropy (EoE) into belief structure, and computes quadratic belief entropy to characterize the complexity of biological systems based on multiple time scales. The influence of inherent complex fluctuation, length bound, correlation of time windows, etc. is considered in the BEoE analysis. Application and discussion demonstrate that BEoE has better accurateness and applicability than many existing entropy algorithms. 

Belief entropy-of-entropy and its application in the cardiac interbeat interval time series analysis

A fuzzy preference-based Dempster-Shafer evidence theory for decision fusion

Interval-valued intuitionistic fuzzy jenson-shannon divergence and its application in multi-attribute decision making

Due to the complexity of the real world, effective consideration of the ambiguity and reliability of information is a challenge that must be addressed by the correct decision of the expert system. Z-number provides us with a good idea because it describes the probability of the random variable and the possibility measure. Recently, Yager presented a soft likelihood function that effectively combines probabilistic evidence to deal with the conflict information. This article generalizes Yager's soft likelihood function based on Z-numbers and proposes a Z-numbers soft likelihood function (ZSLF) decision model. The application examples show the rationality and effectiveness of the method. The comparison and discussion further show the advantages of the ZSLF decision model.

ZSLF: A New Soft Likelihood Function Based on Z-Numbers and Its Application in Expert Decision System

How to effectively deal with uncertain information has traditionally been a concern. The D numbers theory overcomes the limitations of Dempster–Shafer theory and further strengthens the ability of uncertainty modeling. Recently, Yager et al. proposed a soft likelihood function which can effectively combine probability information. Related research has enriched and expanded its connotation, but there are still problems to be solved. This paper conducted further research and proposed a new soft likelihood function based on D numbers. Comparison and discussion illustrate the rationality and superiority of the proposed methodology.

Lili Liu

Papers

ZSLF: A New Soft Likelihood Function Based on Z-Numbers and Its Application in Expert Decision System

A New Soft Likelihood Function Based on D Numbers in Handling Uncertain Information