Anfis Models for Synthesis of Open Supported Coplanar Waveguides

Abstract

Abstract:Most of the traditional clustering algorithms are poor for clustering more complex structures other than the convex spherical sample space. In the past few years, several spectral clustering algorithms were proposed to cluster arbitrarily shaped data in various real applications. However, spectral clustering relies on the dataset where each cluster is approximately well separated to a certain extent. In the case that the cluster has an obvious inflection point within a non-convex space, the spectral clustering algorithm would mistakenly recognize one cluster to be different clusters. In this paper, we propose a novel spectral clustering algorithm called HSC combined with hierarchical method, which obviates the disadvantage of the spectral clustering by not using the misleading information of the noisy neighboring data points. The simple clustering procedure is applied to eliminate the misleading information, and thus the HSC algorithm could cluster both convex shaped data and arbitrarily shaped data more efficiently and accurately. The experiments on both synthetic data sets and real data sets show that HSC outperforms other popular clustering algorithms. Furthermore, we observed that HSC can also be used for the estimation of the number of clusters.

Improved Antlion Optimizer Algorithm and Its Performance on Neuro Fuzzy Inference System

“…These systems are capable of modeling the nonlinear relation between input and output of a system [19]. In the literature, there are different ANFIS applications in areas such as environmental engineering [37], health informatics [7], earth sciences [20], agricultural & biosystems engineering [33], synthesis of production processes [15]. The most important criterion regarding ANFIS structure is to be tuning its antecedent and consequent parameters, this procedure is known as the training phase.…”