The adaptive neuro-fuzzy inference system (ANFIS) is employed in a vast range of applications because of its smoothness (by Fuzzy Control (FC)) and adaptability (by Neural Network (NN)). Although ANFIS is better in nonlinear optimization, two major loopholes need to be addressed thoroughly. They are the curse of dimensionality and computational complexity. To overcome these complications, a novel usage of the ANFIS model is proposed as Cascaded ANFIS in this paper. As the primary source of this algorithm, a general two-input one-output ANFIS algorithm is used. The novel algorithm has two main modules called pair selection and training model. Pair selection is responsible for selecting the best match for the inputs, while the training module generates the output. The cascaded behavior of the novel algorithm generates additional iterations to advance the best solution. Even though the number of parameters that need to be adjusted is increasing at each additional iteration, the complexity of the algorithm may stay stable. Two-hybrid state-of-the-art algorithms are used to compare the performance of the novel algorithm, namely, Particle Swarm Optimization-based ANFIS (ANFIS-PSO) and Genetic Algorithm-based ANFIS (ANFIS-GA). Furthermore, individual performance is presented for seven publicly recognized datasets. The results have demonstrated that, for some datasets, the root means square error (RMSE) can be minimum as 0.0001.

自适应神经模糊推理系统（ANFIS）具有平滑性（通过模糊控制（FC））和适应性（通过神经网络（NN）），因此在广泛的应用中得到了应用。尽管ANFIS在非线性优化方面更好，但仍需要彻底解决两个主要漏洞。它们是维度和计算复杂性的诅咒。为了克服这些复杂性，本文提出了一种新颖的ANFIS模型用法，即级联ANFIS。作为该算法的主要来源，使用了通用的两输入一输出ANFIS算法。新算法具有两个主要模块，称为配对选择和训练模型。配对选择负责为输入选择最佳匹配，而训练模块则生成输出。新算法的级联行为会生成其他迭代，以推进最佳解决方案。即使需要调整的参数数量在每次其他迭代中都在增加，算法的复杂度仍可以保持稳定。使用两种最新的混合算法对新算法的性能进行比较，即基于粒子群优化的ANFIS（ANFIS-PSO）和基于遗传算法的ANFIS（ANFIS-GA）。此外，还针对七个公众认可的数据集展示了个人表现。结果表明，对于某些数据集，均方根误差（RMSE）可以最小为0.0001。使用两种最新的混合算法对新算法的性能进行比较，即基于粒子群优化的ANFIS（ANFIS-PSO）和基于遗传算法的ANFIS（ANFIS-GA）。此外，还针对七个公众认可的数据集展示了个人表现。结果表明，对于某些数据集，均方根误差（RMSE）可以最小为0.0001。使用两种最新的混合算法对新算法的性能进行比较，即基于粒子群优化的ANFIS（ANFIS-PSO）和基于遗传算法的ANFIS（ANFIS-GA）。此外，还针对七个公众认可的数据集展示了个人表现。结果表明，对于某些数据集，均方根误差（RMSE）可以最小为0.0001。