By Vassilis G. Kaburlasos, Gerhard X. Ritter

ISBN-10: 3540726861

ISBN-13: 9783540726869

This eighteen-chapter ebook offers the newest purposes of lattice conception in Computational Intelligence (CI). The e-book makes a speciality of neural computation, mathematical morphology, computer studying, and (fuzzy) inference/logic. The booklet comes out of a unique consultation held through the international Council for Curriculum and guide international convention (WCCI 2006). The articles awarded right here show how lattice idea may well recommend achievable possible choices in sensible clustering, class, development research, and regression functions.

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Extra info for Computational Intelligence Based on Lattice Theory

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In the middle of the same figure we can see how a group of hyperboxes (2-dimensional boxes in this case) can approximate the decision boundaries. Each box can be defined by a dendrite. Complicated figures require a large number of dendrites, in order to achieve satisfactory approximation of the decision boundaries. Note that here the word hyperbox has a more general meaning, since some of the bounding planes of the hyperboxes may have infinite length. The word hyperbox as used in this article includes open hyperboxes.

In: Proc FUZZ-IEEE pp 1296–1301 15. Ritter GX, Iancu L (2003) Single layer feedforward neural network based on lattice algebra. In: Proc IJCNN pp 2887–2892 16. Segev I (1998) Dendritic processing. In: Arbib M (ed) Handbook of Brain Theory and Neural Networks. MIT Press, Cambridge, MA 17. Sejnowski TJ, Qian N (1992) Synaptic integration by electro-diffusion in dendritic spines. In: McKenna T, Davis J, Zornetzer SF (eds) Single Neuron Computation. Academic Press, New York, NY 18. Urcid G (2005) Transformations of neural inputs in lattice dendrite computation.

Each p ξ is a 50 × 50 pixel 256-gray scale image, whereas each qξ is a 30 × 50 pixel 256-gray scale image, where ξ = 1, 2, 3. Using the standard row-scan method, each pattern image p ξ and q ξ was converted into an associative pair of pattern vectors x ξ = (xξ1 , . . , xξ2500 ) ξ and y ξ = (y1ξ , . . , y1500 ) . Thus, for this particular example, we have X = 2500 1 2 3 {x , x , x } ⊂ IR and Y = {y1 , y2 , y3 } ⊂ IR1500 . The patterns illustrated in the top row of Fig. 7). 49min[d(x , x ), d(x , x )] ≈ 92 .

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Computational Intelligence Based on Lattice Theory by Vassilis G. Kaburlasos, Gerhard X. Ritter


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