Automatic design of cellular neural networks by means of genetic algorithms: finding a feature detector

We purpose to find a new beneficial method for accelerating the Decision-Making and classifier support applied on imprecise data. This acceleration can be done by integration between Rough Sets theory, which gives us the minimal set of decision rules, and the Cellular Neural Networks. Our method depends on Genetic Algorithms for designing the cloning template for more accuracy. Some illustrative examples are given to demonstrate the effectiveness of the proposed method, whose advantages and limitations are also discussed.

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Design and training of multilayer discrete time cellular neural networks for antipersonnel mine detection using genetic algorithms

Proceedings of the 2000 6th IEEE International Workshop on Cellular Neural Networks and their Applications (CNNA 2000) (Cat. No.00TH8509) ◽

10.1109/cnna.2000.877356 ◽

2002 ◽

Cited By ~ 6

Author(s):

P. Lopez ◽

M. Balsi ◽

D.L. Vilarino ◽

D. Cabello

Keyword(s):

Neural Networks ◽

Genetic Algorithms ◽

Discrete Time ◽

Cellular Neural Networks ◽

Mine Detection ◽

And Training

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Automatic design of Neural Networks with L-Systems and genetic algorithms - A biologically inspired methodology

The 2011 International Joint Conference on Neural Networks ◽

10.1109/ijcnn.2011.6033360 ◽

2011 ◽

Cited By ~ 5

Author(s):

Lidio Mauro Lima de Campos ◽

Mauro Roisenberg ◽

Roberto Celio Limao de Oliveira

Keyword(s):

Neural Networks ◽

Genetic Algorithms ◽

Automatic Design ◽

Biologically Inspired ◽

L Systems

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Efficient algorithms for space image processing and their realization in cellular neural networks

Kosmìčna nauka ì tehnologìâ ◽

10.15407/knit1998.04.074 ◽

1998 ◽

Vol 4 (4) ◽

pp. 74-84

Author(s):

I.N. Aizenberg ◽

Keyword(s):

Neural Networks ◽

Image Processing ◽

Cellular Neural Networks ◽

Efficient Algorithms ◽

Space Image

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Comparative analysis of intelligent planning methods

Proceedings of the Mavlyutov Institute of Mechanics ◽

10.21662/uim2012.2.048 ◽

2012 ◽

Vol 9 (2) ◽

pp. 53-57 ◽

Cited By ~ 2

Author(s):

O.V. Darintsev ◽

A.B. Migranov

Keyword(s):

Neural Networks ◽

Genetic Algorithms ◽

Fuzzy Logic ◽

Comparative Analysis ◽

Mobile Robots ◽

Sensory Information ◽

Working Environment ◽

Planning Methods ◽

Intelligent Planning

The main stages of solving the problem of planning movements by mobile robots in a non-stationary working environment based on neural networks, genetic algorithms and fuzzy logic are considered. The features common to the considered intellectual algorithms are singled out and their comparative analysis is carried out. Recommendations are given on the use of this or that method depending on the type of problem being solved and the requirements for the speed of the algorithm, the quality of the trajectory, the availability (volume) of sensory information, etc.

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