Algorithmic structure display

CAS Transaction Editor DCRT (NIH) On Line Structure Input System for query formulation (1 ) General processing utilities and a macro processor from Dupont U.S. Army CIDS Substructure Ring Screens and Interactive Search (0) CAS Algorithmic Structure Display (ASD) ( ) and CAS On Line... 

Bruccoleri, R. and Heinrich, G. (1988) An improved algorithm for nucleic acid secondary structure display. Comput. Appl. Biosci. 4, 167-173. 

The same types of graphic analysis for any choice of the matrix V are used. Here an example of the graphic analysis based on an industrial on-line data is presented. The figures illustrate results of static linear PLS model, in which NIR data fix>m an oil refinery is used to model density of the product The vectors in the algorithm are displayed graphically to illustrate the structure and variation in data. The basic plots are 1. ta versus it, The vectors (t,) decompose the data matrix X. Therefore the plots of t, versus tb show us the sample (time) variation in data. Fig 1 reveals that the process (samples) is changing with the time. Arrows in Fig 1 visualise the drift on 1.-4. PLS components. The dynamic behaviour can be clearly seen even on the first two score vectors. Therefore, it cannot be expected that the same model will be valid at the... 

The low-dimensional embedding of both the previously learnt data, and the newly added data, is then improved using an iterative method similar to Ritz acceleration [34], The basic premise is to refine the low-dimensional embedding using the updated structure information found in F ew. The frill algorithm is displayed in Algorithm 1 of [33]. The termination criteria for this refinement step is to either continue for a set number of iterations, or terminate when the error—measured as the Frobenius norm between the previous iteration s estimate and that of the current iteration—falls below a certain tolerance threshold. 

The growing cell structure algorithm is a variant of a Kohonen network, so the GCS displays several similarities with the SOM. The most distinctive feature of the GCS is that the topology is self-adaptive, adjusting as the algorithm learns about classes in the data. So, unlike the SOM, in which the layout of nodes is regular and predefined, the GCS is not constrained in advance to a particular size of network or a certain lattice geometry. 

---