By Franz Aurenhammer, Gerd Stöckl, Emo Welzl (auth.), H. Bieri, H. Noltemeier (eds.)
This quantity offers the court cases of the 7th overseas Workshop on Computational Geometry, CG'91, held on the college of Berne, Switzerland, March 21/22, 1991. Computational geometry isn't really a accurately outlined box. usually, it truly is understood as an almost mathematical self-discipline, dealing commonly with complexity questions touching on geometrical difficulties and algorithms. yet usually too, and maybe more and more, questions of more effective relevance are valuable, reminiscent of applicability, numerical habit and function for every kind of enter dimension. themes thought of in CG'91 comprise: - Generalizations and purposes of the Voronoi diagram - issues of oblong items - direction decision - relocating items - Visibility questions - format difficulties - illustration of spatial gadgets and spatial queries - difficulties in larger dimensions - Implementation questions - family members to synthetic intelligence.
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Additional info for Computational Geometry-Methods, Algorithms and Applications: International Workshop on Computational Geometry CG'91 Bern, Switzerland, March 21–22, 1991 Proceedings
SOFM can learn to detect regularities and correlations in their input and adapt their future responses to that input accordingly. An important feature of SOFM learning algorithm is that it allow neurons that are neighbors to the winning neuron to output values. Thus the transition of output vectors is much smoother than that obtained with competitive layers, where only one neuron has an output at a time. The problem that data visualization attempts to solve is that humans simply cannot visualize high dimensional data.
The inertia weight is employed to control the impact of the previous history of velocities on the current one. Accordingly, the parameter w regulates the trade-oﬀ between the global (wide-ranging) and local (nearby) exploration abilities of the swarm. e. ﬁne-tuning the current search area. A suitable value for the inertia weight w usually provides balance between global and local exploration abilities and consequently results in a reduction of the number of iterations required to locate the optimum solution.
1). From this point of view, the instruction +i is also called a ﬂexible neuron operator with i inputs. , +i (i = 2, 3, 4, . . , N ) is selected, i real values are randomly generated and used for representing the connection strength between the node +i and its children. In addition, two adjustable parameters ai and bi are randomly created as ﬂexible activation function parameters. 2 Flexible Neural Tree Algorithms x1 x2 ω1 ω2 xn ωn 41 f(a,b) +n y Fig. 1 A ﬂexible neuron operator f (ai , bi , x) = e −( x−ai 2 bi ) .
Computational Geometry-Methods, Algorithms and Applications: International Workshop on Computational Geometry CG'91 Bern, Switzerland, March 21–22, 1991 Proceedings by Franz Aurenhammer, Gerd Stöckl, Emo Welzl (auth.), H. Bieri, H. Noltemeier (eds.)