GTM: the generative topographic mapping
Neural Computation
A Developer's Survey of Polygonal Simplification Algorithms
IEEE Computer Graphics and Applications
Self-organizing feature maps for solving location-allocation problems with rectilinear distances
Computers and Operations Research
Transforming pseudo-triangulations
Information Processing Letters
A generative probabilistic approach to visualizing sets of symbolic sequences
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Random triangulations of planar point sets
Proceedings of the twenty-second annual symposium on Computational geometry
Viewpoint-based simplification using f-divergences
Information Sciences: an International Journal
Constructing minimum-interference networks
Computational Geometry: Theory and Applications
Computational Geometry: Theory and Applications
Constrained CVT meshes and a comparison of triangular mesh generators
Computational Geometry: Theory and Applications
Triangulating input-constrained planar point sets
Information Processing Letters
Evolutionary neural networks for practical applications
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Evolutionary neural networks for practical applications
A neural network algorithm to simplify 2D meshes
ICANCM'11/ICDCC'11 Proceedings of the 2011 international conference on applied, numerical and computational mathematics, and Proceedings of the 2011 international conference on Computers, digital communications and computing
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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A 2D triangle mesh simplification model is described in this paper, with the main objective of preserving the shape of the original mesh. The proposed model consists of a self-organizing algorithm whose objective is to generate the positions of the nodes of the simplified mesh; afterwards, a triangulation algorithm is performed to reconstruct the triangles of the new simplified mesh. The self-organizing algorithm is an unsupervised learning algorithm that provides a set of nodes representing the best approximation of the original mesh. An adaptation of the neural network algorithm is proposed with the primary objective to work in the context of urban transport networks. We verify the effectiveness of this model through the design and development of some urban network problems. Specifically, the algorithm is applied to two real problems, the first one is the design of a tramway network in a town, and the second one is the design of an information point network within a real bus transport network.