Download Adaptive and Natural Computing Algorithms: 10th by Dominik Olszewski (auth.), Andrej Dobnikar, Uroš Lotrič, PDF

By Dominik Olszewski (auth.), Andrej Dobnikar, Uroš Lotrič, Branko à ter (eds.)

The two-volume set LNCS 6593 and 6594 constitutes the refereed complaints of the tenth overseas convention on Adaptive and normal Computing Algorithms, ICANNGA 2010, held in Ljubljana, Slovenia, in April 2010. The eighty three revised complete papers offered have been conscientiously reviewed and chosen from a complete of one hundred forty four submissions. the second one quantity comprises forty-one papers equipped in topical sections on development acceptance and studying, smooth computing, structures concept, aid vector machines, and bioinformatics.

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Extra resources for Adaptive and Natural Computing Algorithms: 10th International Conference, ICANNGA 2011, Ljubljana, Slovenia, April 14-16, 2011, Proceedings, Part II

Sample text

All three algorithms require the number of clusters as the input parameter. We set it on the values shown in Table 1. 3 Evaluation of Results Clustering of nine data sets is performed by the proposed GSOM and three other algorithms: EM GMM, CS, and SOMkM. e. ground truth, and a clustering error was computed - it’s minimal, maximum and mean value for 100 runs of each algorithm. 1 GHz processor with 3 GB of memory and MATLAB version R2007b. The results of the experiments are collected in Table 3 and the best ones of GSOM are visualized in Fig.

We further describe our technique for distinguishing loads by matching different signal parameters (step-changes in active and reactive powers and power factor) to known patterns. Computational experiments show the effectiveness of the proposed approach. Keywords: feature extraction and classification, k-nearest neighbors, non-intrusive load monitoring, steady-state signatures, support vector machines. ” This may very well be a sms or email message received on your mobile phone in the near future.

2. As both points are actually BMUs, taken from the SOM, point y can be selected in two ways: either from the neighbors of x, if any of them exist, or as a random point from the set P , not equal to the point x. With probability 1 − p, one of the existing neighbors of the x is randomly chosen and with probability p a random point from P is selected, where p is a parameter of an algorithm. When p is small, the point’s movement is more influenced by its closest neighbors and when p is large, the information of locality is less important.

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