Modelling air-pollution problem by Cellular Neural Network

This paper introduces a new method to solve air-pollution problems using the cellular neural network (CNN). By presenting this problem, the advantages and effects of CNN in parallel computing can be shown. This paper is organized in five parts. After the introduction, the paper gives a short summary...

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Bibliographic Details
Main Authors: Vu Duc Thai, Pham Thuong Cat
Format: Conference Proceeding
Language:English
Subjects:
Air pollution
Automatic control
Boundary conditions
Cellular networks
Cellular neural networks
Cellular Neuron Network-CNN
Dispersion
Humans
Information technology
Partial Differential Equation
Partial differential equations
Pollution
Robotics and automation
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Summary:This paper introduces a new method to solve air-pollution problems using the cellular neural network (CNN). By presenting this problem, the advantages and effects of CNN in parallel computing can be shown. This paper is organized in five parts. After the introduction, the paper gives a short summary of the 2D and 3D cellular neural networks. A description of the air pollution problem by partial differential equation (PDE) is given in the 3 rd part. The proposed method of solving air pollution problem by CNN is presented in part four with considerations on boundary conditions and accuracy. Some conclusions are given in part five. The method proposed in this paper can be used also in the modeling of other processes described by 3D partial deferential equations.
DOI:10.1109/ICARCV.2008.4795676