Adaptive Neuro-Fuzzy Inference System (ANFIS) blends advantages of both Artificial Neural Networks (ANNs) and Fuzzy Logic (FL) in a single framework. It provides accelerated learning capacity and adaptive interpretation capabilities to model complex patterns and apprehends nonlinear relationships. ANFIS has been applied and practiced in various domains and provided solutions to commonly recurring problems with improved time and space complexity. Standard ANFIS has certain limitations such as high computational expense, loss of interpretability in larger inputs, curse of dimensionality, and selection of appropriate membership functions. This paper summarizes that the standard ANFIS is unsuitable for complex human tasks that require precise handling of machines and systems. The state-of-the-art and practice research questions have been discussed, which primarily focus on the applicability of ANFIS in the diversifying field of engineering sciences. We conclude that the standard ANFIS architecture is vastly improved when amalgamated with metaheuristic techniques and further moderated with nature-inspired algorithms through calibration and tuning of parameters. It is significant in adapting and automating complex engineering tasks that currently depend on human discretion, prominent in the mechanical, electrical, and geological fields.

中文翻译：

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现代工程科学中自适应神经模糊推理系统的分类


自适应神经模糊推理系统 (ANFIS) 在单一框架中融合了人工神经网络 (ANN) 和模糊逻辑 (FL) 的优点。它提供加速学习能力和自适应解释能力来建模复杂模式并理解非线性关系。 ANFIS 已在各个领域得到应用和实践，并为常见的重复出现的问题提供了解决方案，并提高了时间和空间复杂度。标准 ANFIS 具有某些局限性，例如高计算成本、较大输入中的可解释性损失、维数灾难以及选择适当的隶属函数。本文总结说，标准 AFIS 不适合需要精确处理机器和系统的复杂人工任务。讨论了最先进的实践研究问题，主要集中在 ANFIS 在工程科学多元化领域的适用性。我们得出的结论是，当与元启发式技术合并并通过参数校准和调整以自然启发的算法进一步调节时，标准 AFIS 架构得到了极大的改进。它对于适应和自动化当前依赖于人类判断力的复杂工程任务具有重要意义，这在机械、电气和地质领域尤为突出。