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One of the design problems when using a SOM is deciding on the size of the map. Too many neurons may cause overfitting of the training patterns, with small clusters containing a few patterns. Alternatively, the final SOM may have succeeded in forming good clusters of similar patterns, but with many neurons with a zero, or close to zero frequency. The frequency of a neuron refers to the number of patterns for which that neuron is the winner, referred to as the best matching neuron (BMN). Too many neurons also cause a substantial increase in computational complexity. Too few neurons, on the other hand, will result in clusters with a high variance among the cluster members. An approach to find near optimal SOM architectures is to start training with a small architecture, and to grow the map when more neurons are needed. One such SOM growing algorithm is given below, assuming a square map structure. Note that the map-growing process coexists with the training process. 1. Initialize the codebook vectors for a small, undersized SOM. 2. Grow the map: (a) Train the SOM for pattern presentations, using any SOM training method. (b) Find the neuron kj with the largest quantization error. (c) Find the furthest immediate neighbor mn in the row-dimension of the map, and the furthest neuron op in the column-dimension. (d) Insert a column between neurons kj and op and a row between neurons kj and mn (this step preserves the square structure of the map). word pdf 417 How to Encode a Tab or Function in a PDF417 in Microsoft Word ...
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Generating and creating specification-compatible PDF-417 barcodes in Microsoft Word documents directly. Download free trial package and view tutorial ... These eigenvectors are not yet normalised to unity, which can be done simply by dividing each by the square root of the sum of its component moduli squared. In the special case when kx = ky = 0 and kz = 0 the HH state vector is simply [1 0]T (i.e. only a particular linear combination of |3/2, 3/2} states is present), while the LH state vector is [0 1]T (only |3/2, 1/2} states are present). 10.6 THE VALENCE BAND IN STRAINED CUBIC SEMICONDUCTORS In this section the influence of strain on the valence band structure will be considered. Strain may be created in several different ways. One way is to subject a semiconductor to hydrostatic pressure, in which case the cubic unit cell would become compressed equally in all three directions. Another is to apply a uniaxial pressure along some axis (e.g. z), while leaving the surfaces perpendicular to the other two axes free, which would result in compressive strain along z and tensile strain along x and y. Yet another way of straining a semiconductor is to grow an epitaxial lattice-mismatched layer of a material on top of a substrate (i.e grow a layer of a material with a different lattice constant from that of the substrate). The thin (epitaxial) layer is forced to acquire the in-plane lattice constant of the substrate, and the perpendicular lattice constant will then also change. Strain is described by 6 components: exx, eyy, ezz, exy, exz, and eyz. It modifies various terms in the Hamiltonian (see equation (10.2)), which then acquire straindependent contributions, see for example [231]: birt ean 13, police word ean 128, free upc barcode font for word, word 2007 code 39 font, word 2010 ean 13, birt data matrix word pdf 417 PDF417 in Microsoft Office Automation | FAQs | PDF417 Barcode ...
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