A Grey Wolf Optimizer for Modular Granular Neural Networks for Human Recognition
Joint Authors
Castillo, Oscar
Melin, Patricia
Sánchez, Daniela
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-26, 26 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-08-14
Country of Publication
Egypt
No. of Pages
26
Main Subjects
Abstract EN
A grey wolf optimizer for modular neural network (MNN) with a granular approach is proposed.
The proposed method performs optimal granulation of data and design of modular neural networks architectures to perform human recognition, and to prove its effectiveness benchmark databases of ear, iris, and face biometric measures are used to perform tests and comparisons against other works.
The design of a modular granular neural network (MGNN) consists in finding optimal parameters of its architecture; these parameters are the number of subgranules, percentage of data for the training phase, learning algorithm, goal error, number of hidden layers, and their number of neurons.
Nowadays, there is a great variety of approaches and new techniques within the evolutionary computing area, and these approaches and techniques have emerged to help find optimal solutions to problems or models and bioinspired algorithms are part of this area.
In this work a grey wolf optimizer is proposed for the design of modular granular neural networks, and the results are compared against a genetic algorithm and a firefly algorithm in order to know which of these techniques provides better results when applied to human recognition.
American Psychological Association (APA)
Sánchez, Daniela& Melin, Patricia& Castillo, Oscar. 2017. A Grey Wolf Optimizer for Modular Granular Neural Networks for Human Recognition. Computational Intelligence and Neuroscience،Vol. 2017, no. 2017, pp.1-26.
https://search.emarefa.net/detail/BIM-1140936
Modern Language Association (MLA)
Sánchez, Daniela…[et al.]. A Grey Wolf Optimizer for Modular Granular Neural Networks for Human Recognition. Computational Intelligence and Neuroscience No. 2017 (2017), pp.1-26.
https://search.emarefa.net/detail/BIM-1140936
American Medical Association (AMA)
Sánchez, Daniela& Melin, Patricia& Castillo, Oscar. A Grey Wolf Optimizer for Modular Granular Neural Networks for Human Recognition. Computational Intelligence and Neuroscience. 2017. Vol. 2017, no. 2017, pp.1-26.
https://search.emarefa.net/detail/BIM-1140936
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1140936