Differential Evolution Evolved RBFNN based automated recognition of Traffic Sign Images

The neural network performances depend entirely upon the quality of learning it has obtained in the past. The conventional gradient-based approach may trap in the local minima, and it may be the cause of premature convergence. To overcome this difficulty, in this paper, a differential evolution that...

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Autores principales:	Manasa R., K. Karibasappa, Manoj Kumar Singh
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Radial basis function Differential evolution Gradient descent Traffic sign recognition Principal component analysis Applied mathematics. Quantitative methods T57-57.97
Acceso en línea:	https://doaj.org/article/892005ba52114c6a8a2784b37c7c5998
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spelling	oai:doaj.org-article:892005ba52114c6a8a2784b37c7c59982021-12-04T04:36:22ZDifferential Evolution Evolved RBFNN based automated recognition of Traffic Sign Images2666-720710.1016/j.rico.2021.100056https://doaj.org/article/892005ba52114c6a8a2784b37c7c59982021-12-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2666720721000333https://doaj.org/toc/2666-7207The neural network performances depend entirely upon the quality of learning it has obtained in the past. The conventional gradient-based approach may trap in the local minima, and it may be the cause of premature convergence. To overcome this difficulty, in this paper, a differential evolution that establishes optimal learning of Radial basis function neural network (RBFNN) has been proposed where different parameters of kernel function and connection weights have been evolved simultaneously. The benefits of evolutionary learning of radial basis function have been evaluated extensively against the gradient-based learning in the classification of benchmark XOR problem. The model integrates principal component analysis (PCA); evolutionary self-adaptive Radial basis function neural network and correlation difference-based decision modules. The PCA has been applied in the pre-processing stage to retrieve the core details available at the input side of the image. A self-adaptive form of radial basis function neural network is used to develop a classifier. For making the learning process more precise and faster, the adjustment parameters considered. One-to-one mapping is used in the learning process to obtain the correlation between input and network parameters. The high recognition efficiency and a high level of decision confidence are the benefits of the proposed hybrid model. With an increase in iteration, the learning error approaches zero value for Differential evolution Adaptive radial basis function (DEARBF). Further, this method is computationally efficient and has shown robustness against visibility limitation because of weather and the aging effect of traffic signboards, proving its applicability in practical life.Manasa R.K. KaribasappaManoj Kumar SinghElsevierarticleRadial basis functionDifferential evolutionGradient descentTraffic sign recognitionPrincipal component analysisApplied mathematics. Quantitative methodsT57-57.97ENResults in Control and Optimization, Vol 5, Iss , Pp 100056- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Radial basis function Differential evolution Gradient descent Traffic sign recognition Principal component analysis Applied mathematics. Quantitative methods T57-57.97
spellingShingle	Radial basis function Differential evolution Gradient descent Traffic sign recognition Principal component analysis Applied mathematics. Quantitative methods T57-57.97 Manasa R. K. Karibasappa Manoj Kumar Singh Differential Evolution Evolved RBFNN based automated recognition of Traffic Sign Images
description	The neural network performances depend entirely upon the quality of learning it has obtained in the past. The conventional gradient-based approach may trap in the local minima, and it may be the cause of premature convergence. To overcome this difficulty, in this paper, a differential evolution that establishes optimal learning of Radial basis function neural network (RBFNN) has been proposed where different parameters of kernel function and connection weights have been evolved simultaneously. The benefits of evolutionary learning of radial basis function have been evaluated extensively against the gradient-based learning in the classification of benchmark XOR problem. The model integrates principal component analysis (PCA); evolutionary self-adaptive Radial basis function neural network and correlation difference-based decision modules. The PCA has been applied in the pre-processing stage to retrieve the core details available at the input side of the image. A self-adaptive form of radial basis function neural network is used to develop a classifier. For making the learning process more precise and faster, the adjustment parameters considered. One-to-one mapping is used in the learning process to obtain the correlation between input and network parameters. The high recognition efficiency and a high level of decision confidence are the benefits of the proposed hybrid model. With an increase in iteration, the learning error approaches zero value for Differential evolution Adaptive radial basis function (DEARBF). Further, this method is computationally efficient and has shown robustness against visibility limitation because of weather and the aging effect of traffic signboards, proving its applicability in practical life.
format	article
author	Manasa R. K. Karibasappa Manoj Kumar Singh
author_facet	Manasa R. K. Karibasappa Manoj Kumar Singh
author_sort	Manasa R.
title	Differential Evolution Evolved RBFNN based automated recognition of Traffic Sign Images
title_short	Differential Evolution Evolved RBFNN based automated recognition of Traffic Sign Images
title_full	Differential Evolution Evolved RBFNN based automated recognition of Traffic Sign Images
title_fullStr	Differential Evolution Evolved RBFNN based automated recognition of Traffic Sign Images
title_full_unstemmed	Differential Evolution Evolved RBFNN based automated recognition of Traffic Sign Images
title_sort	differential evolution evolved rbfnn based automated recognition of traffic sign images
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/892005ba52114c6a8a2784b37c7c5998
work_keys_str_mv	AT manasar differentialevolutionevolvedrbfnnbasedautomatedrecognitionoftrafficsignimages AT kkaribasappa differentialevolutionevolvedrbfnnbasedautomatedrecognitionoftrafficsignimages AT manojkumarsingh differentialevolutionevolvedrbfnnbasedautomatedrecognitionoftrafficsignimages
_version_	1718372924991209472

Differential Evolution Evolved RBFNN based automated recognition of Traffic Sign Images

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