Self-organization method

Farlow, S. J. (1984). Self-organizing method in modelling GMDH-type algorithm. Mareel Dekker. New York. 

The major disadvantage of CNTs, however, is their random distribution, which clearly hampers their utilization as a replacement for silicon as a substrate. This leaves two options for carbon-electronics either self-organization methods for CNTs or carbon substrates , thin layers with similar properties to CNTs. 

An observation of the results of cross-validation revealed that all but one of the compounds in the dataset had been modeled pretty well. The last (31st) compound behaved weirdly. When we looked at its chemical structure, we saw that it was the only compound in the dataset which contained a fluorine atom. What would happen if we removed the compound from the dataset The quahty ofleaming became essentially improved. It is sufficient to say that the cross-vahdation coefficient in-CTeased from 0.82 to 0.92, while the error decreased from 0.65 to 0.44. Another learning method, the Kohonen s Self-Organizing Map, also failed to classify this 31st compound correctly. Hence, we had to conclude that the compound containing a fluorine atom was an obvious outlier of the dataset. 

Kohonen networks, also known as self-organizing maps (SOMs), belong to the large group of methods called artificial neural networks. Artificial neural networks (ANNs) are techniques which process information in a way that is motivated by the functionality of biological nervous systems. For a more detailed description see Section 9.5. 

The Kohonen network or self-organizing map (SOM) was developed by Teuvo Kohonen [11]. It can be used to classify a set of input vectors according to their similarity. The result of such a network is usually a two-dimensional map. Thus, the Kohonen network is a method for projecting objects from a multidimensional space into a two-dimensional space. This projection keeps the topology of the multidimensional space, i.e., points which are close to one another in the multidimensional space are neighbors in the two-dimensional space as well. An advantage of this method is that the results of such a mapping can easily be visualized. 

The synthesis of bimetallic nanoparticles is mainly divided into two methods, i.e., chemical and physical method, or bottom-up and top-down method. The chemical method involves (1) simultaneous or co-reduction, (2) successive or two-stepped reduction of two kinds of metal ions, and (3) self-organization of bimetallic nanoparticle by physically mixing two kinds of already-prepared monometallic nanoparticles with or without after-treatments. Bimetallic nanoparticle alloys are prepared usually by the simultaneous reduction while bimetallic nanoparticles with core/shell structures are prepared usually by the successive reduction. In the preparation of bimetallic nanoparticles, one of the most interesting aspects is a core/shell structure. The surface element plays an important role in the functions of metal nanoparticles like catal5dic and optical properties, but these properties can be tuned by addition of the second element which may be located on the surface or in the center of the particles adjacent to the surface element. So, we would like to use following marks to inscribe the bimetallic nanoparticles composed of metal 1, Mi and metal 2, M2. 

However, the method suffers from two notable disadvantages. First, a self-organizing map is slow to train. During each epoch, every data point in every sample pattern in the database must be compared in turn with the corresponding weight in the vector at every node. Table 4.1 shows how quickly the total number of comparisons required in the training of a SOM grows as the scale of a problem increases. 

Another group has evaluated self-organizing maps [63] and shape/ pharmacophore models [64]. They developed a new method termed SQUIRREL to compare molecules in terms of both shape and pharmacophore points. Thus from a commercial library of 199,272 compounds, 1926 were selected based on self-organizing maps trained on peroxisome proliferator-activated receptor a (PPARa) "activity islands." The compounds were further evaluated with SQUIRREL and 7 out of 21 molecules selected were found to be active in PPARa. Furthermore, a new virtual screening technique (PhAST) was developed based on representation of molecules as text strings that describe their pharmacophores [65]. 

Traditional methods for fabricating nano-scaled arrays are usually based on lithographic techniques. Alternative new approaches rely on the use of self-organizing templates. Due to their intrinsic ability to adopt complex and flexible conformations, proteins have been used to control the size and shape, and also to form ordered two-dimensional arrays of nanopartides. The following examples focus on the use of helical protein templates, such as gelatin and collagen, and protein cages such as ferritin-based molecules. 

Synthesis of the first mesoionic nematic and smectic A liquid crystals derived from sydnones has been described and their self-organization into liquid crystal phases has been studied by optical, calorimetric, and powder X-ray diffraction methods <2005CC1552>. 

TAMAYO, P., SLONIM, D MESIROV, J., ZHU, Q., KITAREEWAN, S., DMITROVSKY, E., LANDER, E.S., GOLUB, T.R., Interpreting patterns of gene expression with self-organizing maps methods and application to hematopoietic differentiation, Proc. Natl. Acad. Sci. USA, 1999,96,2907-2912. 

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