Contribution-Factor based Fuzzy Min-Max Neural Network: Order-Dependent Clustering for Fuzzy System Identification
نویسندگان
چکیده
منابع مشابه
General fuzzy min-max neural network for clustering and classification
This paper describes a general fuzzy min-max (GFMM) neural network which is a generalization and extension of the fuzzy min-max clustering and classification algorithms developed by Simpson. The GFMM method combines the supervised and unsupervised learning within a single training algorithm. The fusion of clustering and classification resulted in an algorithm that can be used as pure clustering...
متن کاملFuzzy min-max neural network based decision trees
This paper presents a new decision tree learning algorithm, fuzzy min-max decision tree (FMMDT) based on fuzzy min-max neural networks. In contrast with traditional decision trees in which a single attribute is selected as the splitting test, the internal nodes of the proposed algorithm contain a fuzzy min-max neural network. In the proposed learning algorithm, the exibility inherent in the fuz...
متن کاملData Centroid Based Multi-Level Fuzzy Min-Max Neural Network
Recently, a multi-level fuzzy min max neural network (MLF) was proposed, which improves the classification accuracy by handling an overlapped region (area of confusion) with the help of a tree structure. In this brief, an extension of MLF is proposed which defines a new boundary region, where the previously proposed methods mark decisions with less confidence and hence misclassification is more...
متن کاملAgglomerative Learning Algorithms for General Fuzzy Min-Max Neural Network
In this paper two agglomerative learning algorithms based on new similarity measures defined for hyperbox fuzzy sets are proposed. They are presented in a context of clustering and classification problems tackled using a general fuzzy min-max (GFMM) neural network. The proposed agglomerative schemes have shown robust behaviour in presence of noise and outliers and insensitivity to the order of ...
متن کاملFuzzy Min-Max Neural Network for Image Segmentation
In this work a new fuzzy min-max neural network for color image segmentation, called FMMIS neural network, is proposed. The FMMIS algorithm uses seed pixels to grow hyperboxes, and a criterion of homogeneity for controlling the size of these hyperboxes. The algorithm has been implemented for 2D images and tested on the segmentation of live and dead knots in images of wood boards. On a test set,...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: International Journal of Computational Intelligence Systems
سال: 2018
ISSN: 1875-6883
DOI: 10.2991/ijcis.11.1.57