Department of Electrical and Computer Engineering, University of Birjand, Birjand, Iran.
Network. 2019 Feb-Nov;30(1-4):31-57. doi: 10.1080/0954898X.2019.1637953. Epub 2019 Aug 26.
There is a variety of fuzzy classifiers, one of which is Adaptive Neuro-Fuzzy Inference system (ANFIS) classifier. One of the main challenges in designing such data classifiers is selection of effective and appropriate type and location of membership functions and its training method to reduce the classification error. In this paper, a new technique (based on intelligent methods) is presented and implemented to select and locate the membership functions and simultaneous training using a new method based on Inclined Planes System Optimization (IPO) to minimize errors of an ANFIS classifier for the first time. The presented method is evaluated for classification of data sets with different reference classes and different length feature vectors, which have acceptable complexity. According to the results of the research, the presented method has a higher level of accuracy and efficiency in selecting the type and location of membership functions (based on intelligent methods) and simultaneous training with IPO, compared to other methods, such as particle swarm optimization, genetic algorithm, differential evolution, and ACOR algorithms.
有各种各样的模糊分类器，其中之一是自适应神经模糊推理系统（ANFIS）分类器。设计此类数据分类器的主要挑战之一是选择有效且合适的隶属函数类型和位置及其训练方法，以减少分类误差。在本文中，提出并实现了一种新技术（基于智能方法），用于选择和定位隶属函数，并使用基于倾斜平面系统优化（IPO）的新方法同时进行训练，以首次最小化 ANFIS 分类器的误差。所提出的方法用于对具有不同参考类别的数据集和不同长度特征向量的分类进行评估，其复杂度可以接受。根据研究结果，与粒子群优化、遗传算法、差分进化和 ACOR 算法等其他方法相比，所提出的方法在基于智能方法选择隶属函数的类型和位置以及与 IPO 同时进行训练方面具有更高的准确性和效率。
