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Equivalence and stability of two-layered cellular neural network solving Saint Venant 1D equation
11th International Conference on Control, Automation, Robotics and Vision, ICARCV 2010 Số , năm 2010 (Tập , trang 704-709)
DOI: 10.1109/ICARCV.2010.5707870
Tài liệu thuộc danh mục: Scopus
Conference Paper
English
Từ khóa: 1-D models; CNN models; CNN template; Partial differential; Saint Venant equation; Saint-Venant; Solving Set; Topological equivalence; Water flow; Water flows; Cellular neural networks; Computational fluid dynamics; Computer simulation; Computer vision; Flow of water; Hydraulics; Image segmentation; Network layers; Robotics; System stability; Wireless networks; Partial differential equations
Tóm tắt tiếng anh
Cellular Neural Network (CNN) has been used for solving Partial Differential Equations (PDE). However, the equivalence and stability of system should be considered carefully in a particular problem. In this paper, we introduce the model CNN for solving set of two PDEs describing water flow channels (called Saint Venant equation). We analyze the approximation and topological equivalence issues between Cellular Partial Difference Differential Equation (CPDDE) and its original PDEs. The stability of CNN system is also proved from discovering the equilibrium of the state and output of each cell. The paper has 4 parts. After introduction, part 2 gives a two-layered CNN 1D model for solving PDE Saint Venant equation. In the part 3 the equivalence and stability of the CNN model are proved, then simulation using FPGA. The conclusions are given in the last part. �2010 IEEE.
