Applied Soft Computing Technologies: The Challenge of by Ajith Abraham

By Ajith Abraham

This quantity provides the complaints of the ninth on-line international convention on tender Computing in commercial functions, hung on the realm large net in 2004. It contains lectures, unique papers and tutorials awarded through the convention. The ebook brings jointly awesome study and advancements in gentle computing, together with evolutionary computation, fuzzy good judgment, neural networks, and their fusion, and its purposes in technology and know-how.

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Additional resources for Applied Soft Computing Technologies: The Challenge of Complexity (Advances in Soft Computing)

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Which means out of Parallel Neuro Classifier for Weld Defect Classification 47 2493 examples only around 135 examples are misclassified, which is reasonable when comparing to such a large amount of data set. Table 3 shows the number of misclassified examples in the testing dataset, by each evaluation methods. Table 2. Comparison of computational time and error for different evaluation methods Sr. No. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 Avg. Error Std. Dev. 37 11min. 22 sec. 01 101 min.

Y. Lin, Data mining and machine oriented modeling: a granular computing approach, J. of Applied Intelligence, 13, 2, 2000, 113-124. 34 W. Pedrycz 7. J. Lukasiewicz, Philosophische Bemerkungen zu mehrwertigen Systemen des Aussagenkalk, C. R. Soc. Sci. Lettres de Varsovie, 23, 1930, 51-77. 8. M. Mendel, On a 50% savings in the computation of the centroid of a symmetrical interval type-2 fuzzy set , Information Sciences, In press, Available online 2 July 2004. 9. R. Moore, Interval Analysis, Prentice Hall, Englewood Cliffs, NJ, 1966.

1 Selection of Classifier Neural networks are usually employed to classify patterns based on learning from examples. , are available. The singlelayer Hopfield and Hamming nets are normally used with binary input and output under supervised learning. The Carpenter/Grossberg net, however, implements unsupervised learning. The single-layer perceptron can be used with multi-value input and output in addition to binary data. A serious disadvantage of the single-layer network is that complex decision may not be possible.

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