ABSTRACT 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.

中文翻译：

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ANFIS分类器的自动启发式完整设计

摘要 有多种模糊分类器，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适当的隶属函数类型和位置及其训练方法以减少分类错误。在本文中，提出并实施了一种新技术（基于智能方法）来选择和定位隶属函数并使用基于斜面系统优化（IPO）的新方法进行同步训练，以最大限度地减少 ANFIS 分类器的误差。时间。所提出的方法用于对具有不同参考类别和不同长度特征向量的数据集进行分类，这些数据集具有可接受的复杂度。根据研究结果，