方法对比

	Belief Rule Base	证据的Dempster-Shafer理论	模糊认知图 (FCM)
领域	软计算	软计算	软计算
方法族	Machine learning	Machine learning	Process / pipeline
起源年份	2006	1976	1986
提出者	Jian-Bo Yang et al.	Arthur P. Dempster & Glenn Shafer	Bart Kosko
类型	Expert-system inference with belief distributions	Uncertainty calculus for combining evidence	Fuzzy causal/feedback network for scenario analysis
开创性文献	Yang, J.-B., Liu, J., Wang, J., Sii, H.-S., & Wang, H.-W. (2006). Belief rule-base inference methodology using the evidential reasoning approach—RIMER. IEEE Transactions on Systems, Man, and Cybernetics—Part A, 36(2), 266–285.	Dempster, A. P. (1967). Upper and lower probabilities induced by a multivalued mapping. The Annals of Mathematical Statistics, 38(2), 325–339.	Kosko, B. (1986). Fuzzy cognitive maps. International Journal of Man-Machine Studies, 24(1), 65–75.
别名	RIMER, Belief Rule-Based System, BRB System, İnanç Kural Tabanlı Çıkarım	evidence theory, belief functions, evidential reasoning, Dempster-Shafer kanıt teorisi	FCM, Kosko cognitive map, causal cognitive map, bulanık bilişsel haritalar
相关	3	4	4
摘要	Belief Rule Base (BRB), introduced by Yang et al. in 2006 under the RIMER framework, is an expert-system inference methodology that extends classical if-then rules by attaching belief degree distributions to rule consequents. It combines rule-based reasoning with the Evidential Reasoning (ER) approach, enabling the representation and propagation of uncertainty, incompleteness, and vagueness in complex decision problems across engineering, risk assessment, and management domains.	Dempster-Shafer theory is a mathematical framework for reasoning under uncertainty that generalizes Bayesian probability by representing ignorance explicitly. Instead of forcing a single probability on each hypothesis, it assigns belief mass to sets of hypotheses and derives a belief-plausibility interval, and it provides Dempster's rule for fusing evidence from multiple independent sources. Developed from Arthur Dempster's 1967 work and Glenn Shafer's 1976 monograph, it underpins evidential reasoning and sensor/decision fusion.	A fuzzy cognitive map, introduced by Bart Kosko in 1986, represents a system as a network of concepts connected by signed, weighted causal links, and simulates how the concepts influence one another over time. By combining the intuitive structure of a cognitive map with fuzzy weights and iterative activation, FCMs let experts encode causal knowledge and then run what-if scenarios — making them popular for policy analysis, strategic decision-making, and modelling complex socio-technical systems.