Fuzzy rough granular neural networks, fuzzy granules, and classification

Ganivada, Avatharam; Dutta, Soumitra; Pal, Sankar K.

doi:10.1016/j.tcs.2011.05.038

Cited by 52 publications

(33 citation statements)

References 23 publications

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“…The second situation occurs, for instance, when there are noisy data or we encounter qualitative assessments provided by human experts. Here, granulation of information allows modeling the precision of indirect measurements, providing a computationally appealing view of knowledge (Ganivada et al 2011).…”

Section: Why Granular?mentioning

confidence: 99%

“…(Pedrycz and Gomide 2007). Fuzzy sets are adopted as the granular information in (Zhang et al 2000;Dick et al 2013;Sánchez et al 2015a;Oh et al 2013;Ganivada et al 2011;Leite et al 2013;Pedrycz and Vukovich 2001;Pedrycz et al 2008;Park et al 2012;Zhang et al 2008;Nandedkar and Biswas 2009;Vasilakos and Stathakis 2005;Marcek and Marcek 2008).…”

Section: Fuzzy Setsmentioning

confidence: 99%

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A study of granular computing in the agenda of growth of artificial neural networks

Song

Wang

2016

Granul. Comput.

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Granular neural networks (GNN) are designed to process complex non-numerical data or the combination of numerical and non-numerical data. The concept of ''granules'' here refers to various data groups which are drawn together by the criteria of similarity or functionality. Granular neural networks are being used in areas of knowledge discovery, pattern recognition, etc. This paper carries out a comprehensive review of articles that involve a comparative study of different types of granular neural networks and their application. This study aims to give useful insight into the capability of granular neural networks.

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Section: Why Granular?mentioning

confidence: 99%

Section: Fuzzy Setsmentioning

confidence: 99%

A study of granular computing in the agenda of growth of artificial neural networks

Song

Wang

2016

Granul. Comput.

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“…The integration of IG constructs and CI systems, such as pattern recognition and control systems, is nowadays wellestablished. For instance, granular neural networks offer an interesting example (Ding et al 2014;Zhang et al 2008;Ganivada et al 2011;Song and Pedrycz 2013). Granular neural networks are basically extensions of typical artificial neural network architectures, which incorporate a mechanism of information granulation at the weights level or within the neuron model.…”

Section: Granular Computing As a General Data Analysis Frameworkmentioning

confidence: 99%

Granular computing, computational intelligence, and the analysis of non-geometric input spaces

Livi

Sadeghian

2015

Granul. Comput.

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Data granulation emerged as an important paradigm in modeling and computing with uncertainty, exploiting information granules as the main mathematical constructs involved in the context of granular computing. In this paper, we comment on the importance of data granulation in computational intelligence methods. Toward this aim, we discuss also the peculiar aspects related to the analysis of non-geometric patterns, which have recently attracted considerable attention by researchers. As a conclusion, we elaborate over the fundamental, conceptual problems underlying the process of data granulation, which drive the quest for a sound theory of granular computing.

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“…GNN with the high-speed evolutionary interval learning is designed by Zhang et al, to deal with different membership functions of the same linguistic term (Zhang et al, 2008). Ganivada et al introduced a fuzzy rough GNN (FRGNN) model based on the multilayer perceptron using a back-propagation algorithm for the fuzzy classification of patterns (Ganivada et al, 2011). Leite et al proposed an evolving GNN (eGNN) model, which can build interpretable multi-sized local models using fuzzy neurons for information fusion using an online incremental learning algorithm (Leite et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

A new method for constructing granular neural networks based on rule extraction and extreme learning machine

Xiaoli

Wang

Ding

et al. 2015

Pattern Recognition Letters

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Fuzzy rough granular neural networks, fuzzy granules, and classification

Cited by 52 publications

References 23 publications

A study of granular computing in the agenda of growth of artificial neural networks

A study of granular computing in the agenda of growth of artificial neural networks

Granular computing, computational intelligence, and the analysis of non-geometric input spaces

A new method for constructing granular neural networks based on rule extraction and extreme learning machine

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