The latest research in the field of casual relations using fuzzy cognitive maps utilizes various adaptive supervised learning techniques. These supervised methods provide promising data understanding and processing the fuzzy outcomes. This article contributes in the modern research by providing an adaptive review of different knowledge-oriented methods for the establishment of Fuzzy Cognitive Maps (FCM) based Causal Inference Relations exploiting various domains. A comprehensive review is presented in this article that significantly introduces the artificial intelligence-based methodologies and their progress in the field of casual inference relations. An ensembled-FCM based approach is presented in this article which is analysed and compared to the conventional Hebbian based, error-driven and hybrid approaches. The performance parameters evaluated for the testing phase of E-FCM provides the precision, recall and accuracy rates of 98.05 %, 97.83 % and 94.54 % respectively, while prediction errors observed MSE value of 0.034 ± 0.005 with relevant reduction in RSME and MSE of 0.052 ± 0.007 and 0.085 ± 0.009 respectively. Further, a systematic categorization of existing techniques is presented based on the taxonomy of different techniques from varying perspectives like its extension and application domains. Finally, an outline of the potential research directions in this field is presented, establishing a clear understanding of development in this domain.

使用模糊认知图的偶然关系领域的最新研究利用了各种自适应监督学习技术。这些监督方法提供了有希望的数据理解和处理模糊结果。本文通过对不同的面向知识的方法进行适应性回顾，为现代研究做出贡献，这些方法用于建立利用各个领域的基于模糊认知图 (FCM) 的因果推理关系。本文进行了全面回顾，重点介绍了基于人工智能的方法及其在随意推理关系领域的进展。本文介绍了一种基于集成 FCM 的方法，该方法与传统的基于 Hebbian、错误驱动和混合方法进行了分析和比较。E-FCM 测试阶段评估的性能参数分别提供了 98.05%、97.83% 和 94.54% 的精度、召回率和准确率，而预测误差观察到 MSE 值为 0.034 ± 0.005，RSME 和 MSE 相关降低 0.052分别为 ± 0.007 和 0.085 ± 0.009。此外，基于不同技术的分类，从不同的角度（如其扩展和应用领域）对现有技术进行了系统分类。最后，概述了该领域的潜在研究方向，建立了对该领域发展的清晰认识。005，RSME 和 MSE 的相关降低分别为 0.052 ± 0.007 和 0.085 ± 0.009。此外，基于不同技术的分类，从不同的角度（如其扩展和应用领域）对现有技术进行了系统分类。最后，概述了该领域的潜在研究方向，建立了对该领域发展的清晰认识。005，RSME 和 MSE 的相关降低分别为 0.052 ± 0.007 和 0.085 ± 0.009。此外，基于不同技术的分类，从不同的角度（如其扩展和应用领域）对现有技术进行了系统分类。最后，概述了该领域的潜在研究方向，建立了对该领域发展的清晰认识。