Download Aging and Radiation Effects in Stockpile Electronics by Hartman, E.F.; Sandia National Laboratories.; United States. PDF

By Hartman, E.F.; Sandia National Laboratories.; United States. Dept. of Energy.; United States. Dept. of Energy. Office of Scientific and Technical Information

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The multiplier factor N is also referred to as the number of cycles or epochs. 6 shows an example of the prototype weights adaptation (input layer) on the mixture of five Gaussians over 100,000 iteration. 6. Illustration of the SOM learning iterations on a mixture of five spherical Gaussians in two dimensions. The nodes, which in this example are projected into the data space, are depicted as filled circles. The lines connecting the nodes indicate adjacent nodes in the map (Kohonen layer). The lines and nodes’ colors, from light to dark, are used to indicate the sequence of iterations, starting from the random initialization, until the convergence state, after 100,000 iterations the nodes distribution at different stages of the algorithms, whereas black dots depict the dinal convergence positions.

1. Pole-based overlapping clustering The pole-based overlapping clustering (PoBOC) is an overlapping, graph-based clustering technique proposed by Cleizou [CLE 04a, CLE 04b]. The algorithm takes the matrix of object dissimilarities as single input and builds the output clusters in four main steps: (i) definition of dissimilarity graph, (ii) construction of poles, (iii) pole restriction, and (iv) affectation of objects to poles. 1. Definition of a dissimilarity graph Let X denote the set of objects in the dataset and D the dissimilarity matrix, computed over X .

5. For each one of the remaining objects, xi ∈ C , calculate the average distance to the rest of objects in C and C . Let this average distances be denoted as davg (xi , C ) and davg (xi , C ). If davg (xi , C ) > davg (xi , C ), the pattern has more affinity to the new formed cluster. Select the pattern x ˆ whose affinity to C is the highest in comparison to C . 1] Remove x ˆ from C and attach to C . 6. Repeat Step 5 until no object in C can be found such that davg (xi , C ) > davg (xi , C ). At this point, the partition of the cluster C is finished.

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