By J. S. Wells, G. E. Streit, F. R. Petersen
Read or Download Application of Infrared Frequency Synthesis Techniques With Metal-Insulator-Metal Diodes to the Spin Flip Raman laser PDF
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Additional resources for Application of Infrared Frequency Synthesis Techniques With Metal-Insulator-Metal Diodes to the Spin Flip Raman laser
5). 2626 seconds, which is three times larger than the standard deviation of CCT values in the learning set. In short, the initial output values are random. 00056 seconds. 4 milliseconds which is actually smaller than the accuracy of the CCT values determined by numerical simulation. Thus, we deem that we are close to the global minimum. All in all, the parameter adaptation process took 730 CPU seconds on a Sparc 10 SUN workstation. 39876) Validation. In order to evaluate the reliability of this approximation, we have used the MLP to predict the CCTs of the 2000 test states.
6 Refinements. There are many refinements of the IDIOT method of interest in the context of security assessment. g. to represent power system topology) together with numerical ones. They may also be generalized to an arbitrary number of (security) classes and to tests with more than two outcomes. Another interesting extension consists of using linear combinations instead of single attribute (orthogonal) splits, yielding so-called "oblique" decision trees. They are useful when there are strong interactions among different candidate attributes.
Adapted from [Weh95b J. Graphs, trees and directed trees Example tree and attribute space representation Partitioning of qualitative vs hierarchical attributes. 2% Severity regression tree: N = 2775, At = 913, AE = 22MW Fuzzy transient stability classes Main differences between crisp and fuzzy decision trees Crisp vs fuzzy decision trees Hybrid DT-ANN approach Decision tree for transient stability assessment MLP resulting from the translation of the DT of Fig. 1 Sample of OMIB operating states Error rates (%) of KNN classifiers Attribute statistics of the OMIB problem Perceptron learning algorithm Weights and activations of OMIB MLP for state 1 Kohonen self-organizing map learning algorithm Rules corresponding to the tree of Fig.