Previous criticisms of knowledge-based fuzzy logic modelling have identified some of its limitations and revealed weaknesses regarding the development of fuzzy sets, the integration of expert knowledge, and the outcomes of different defuzzification processes.

We show here how expert disagreement and fuzzy logic mechanisms associated with the rule development and combinations can positively or adversely affect model performance and the interpretation of results. We highlight how expert disagreement can induce uncertainty into model outputs when defining fuzzy sets and selecting a defuzzification method. We present a framework to account for sources of error and bias and improve the performance and robustness of knowledge-based fuzzy logic models. We recommend to 1) provide clear/unambiguous instructions on model development, processes and objectives, including the definition of input variables and fuzzy sets, 2) incorporate the disagreement among experts into the analysis, 3) increase the use of short rules and the OR operator to reduce complexity, and 4) improve model performance and robustness by using narrow fuzzy sets for extreme values of input variables to expand the universe of discourse adequately. Our framework is focused on fuzzy logic models but can be applied to all knowledge-based models that require expert judgment, including expert systems, decision trees and (fuzzy) Bayesian inference systems.

以前对基于知识的模糊逻辑建模的批评已经确定了它的一些局限性，并揭示了在模糊集的发展、专家知识的整合以及不同解模糊过程的结果方面的弱点。

我们在这里展示了与规则开发和组合相关的专家分歧和模糊逻辑机制如何对模型性能和结果解释产生正面或负面影响。我们强调了在定义模糊集和选择去模糊化方法时专家分歧如何将不确定性引入模型输出。我们提出了一个框架来解释错误和偏差的来源，并提高基于知识的模糊逻辑模型的性能和鲁棒性。我们建议 1) 提供关于模型开发、过程和目标的清晰/明确说明，包括输入变量和模糊集的定义，2) 将专家之间的分歧纳入分析，3) 增加使用简短规则和 OR操作员以降低复杂性，4) 通过对输入变量的极值使用窄模糊集来充分扩展讨论范围，从而提高模型性能和鲁棒性。我们的框架专注于模糊逻辑模型，但可以应用于所有需要专家判断的基于知识的模型，包括专家系统、决策树和（模糊）贝叶斯推理系统。