Lovely Professional University, Phagwara, Punjab, India.
Government Bikram College of Commerce, Patiala, Punjab, India.
Comput Intell Neurosci. 2021 Sep 3;2021:6455592. doi: 10.1155/2021/6455592. eCollection 2021.
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.
自适应神经模糊推理系统（ANFIS）将人工神经网络（ANNs）和模糊逻辑（FL）的优势融合在一个单一的框架中。它提供了加速学习能力和自适应解释能力，能够模拟复杂的模式并理解非线性关系。ANFIS 已在各个领域得到应用和实践，并通过改进时间和空间复杂度为常见问题提供了解决方案。标准的 ANFIS 存在一定的局限性，例如计算开销大、较大输入时的可解释性丧失、维度诅咒以及合适的隶属函数的选择。本文总结了标准的 ANFIS 不适合需要精确处理机器和系统的复杂人类任务。讨论了最新技术和实践研究问题，主要集中在 ANFIS 在工程科学多样化领域的适用性。我们得出结论，当与元启发式技术结合并通过参数校准和调整进一步与受自然启发的算法结合时，标准的 ANFIS 架构会得到极大的改进。这对于适应和自动化复杂的工程任务非常重要，这些任务目前依赖于人类的判断力，在机械、电气和地质等领域非常突出。
