Title
Fuzzy logic in the gravitational search algorithm enhanced using fuzzy logic with dynamic alpha parameter value adaptation for the optimization of modular neural networks in echocardiogram recognition
Abstract
Approach for the optimization of modular neural networks with the gravitational search algorithm using fuzzy logic for pattern recognition.The proposed method is applied for the recognition of medical images.In this case, we are using a database of echocardiograms that contains images of disease and healthy patients to test the approach. In this paper the main goal is to find the optimal architecture of modular neural networks, which means finding out the optimal number of modules, layers and nodes of the neural network. The fuzzy gravitational search algorithm with dynamic parameter adaptation is used for optimizing the modular neural network in a particular pattern recognition application. The proposed method is applied to medical images in echocardiogram recognition. One of the most common methods for detection and analysis of diseases in the human body, by physicians and specialists, is the use of medical images. Simulation results of the proposed approach in echocardiogram recognition show the advantages of using the fuzzy gravitational search in the optimization of modular neural networks. In this case the proposed approach provides a very good 99.49% echocardiogram recognition rate.
Year
DOI
Venue
2015
10.1016/j.asoc.2015.08.034
Applied Soft Computing
Keywords
Field
DocType
Modular neural network,Gravitational search algorithm,Pattern recognition,Echocardiograms,GSA,Fuzzy logic
Neuro-fuzzy,Modular neural network,Fuzzy logic,Time delay neural network,Artificial intelligence,Modular design,Artificial neural network,Gravitational search algorithm,Machine learning,Mathematics
Journal
Volume
Issue
ISSN
37
C
1568-4946
Citations 
PageRank 
References 
5
0.45
31
Authors
4
Name
Order
Citations
PageRank
Beatriz González150.45
Fevrier Valdez2322.46
Patricia Melin350.79
German Prado-Arechiga4273.70