Fuzzy rough granular neural networks, fuzzy granules, and classification
نویسندگان
چکیده
منابع مشابه
Fuzzy rough granular neural networks, fuzzy granules, and classification
We introduce a fuzzy rough granular neural network (FRGNN) model based on the multilayer perceptron using a back-propagation algorithm for the fuzzy classification of patterns. We provide the development strategy of the network mainly based upon the input vector, initial connection weights determined by fuzzy rough set theoretic concepts, and the target vector. While the input vector is describ...
متن کاملA Novel Fuzzy Rough Granular Neural Network for Classification
A novel fuzzy rough granular neural network (NFRGNN) based on the multilayer perceptron using backpropagation algorithm is described for fuzzy classification of patterns. We provide a development strategy of knowledge extraction from data using fuzzy rough set theoretic techniques. Extracted knowledge is then encoded into the network in the form of initial weights. The granular input vector is ...
متن کاملFuzzy Rough Granular Neural Networks for Pattern Analysis
Granular computing is a computational paradigm in which a granule represents a structure of patterns evolved by performing operations on the individual patterns. Two granular neural networks are described for performing the pattern analysis tasks like classification and clustering. The granular neural networks are designed by integrating fuzzy sets and fuzzy rough sets with artificial neural ne...
متن کاملFuzzy rough granular self-organizing map and fuzzy rough entropy
A fuzzy rough granular self-organizingmap (FRGSOM) involving a 3-dimensional linguistic vector and connection weights, defined in an unsupervised manner, is proposed for clustering patterns having overlapping regions. Each feature of a pattern is transformed into a 3-dimensional granular space using a π-membership function with centers and scaling factors corresponding to the linguistic terms l...
متن کاملClass-dependent rough-fuzzy granular space, dispersion index and classification
A new rough-fuzzy model for pattern classification based on granular computing is described in the present article. In this model, we propose the formulation of class-dependent granules in fuzzy environment. Fuzzy membership functions are used to represent the feature-wise belonging to different classes, thereby producing fuzzy granulation of the feature space. The fuzzy granules thus generated...
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: Theoretical Computer Science
سال: 2011
ISSN: 0304-3975
DOI: 10.1016/j.tcs.2011.05.038