Read e-book online Applied Soft Computing Technologies: The Challenge of PDF

By Ajith Abraham

ISBN-10: 3540316493

ISBN-13: 9783540316497

ISBN-10: 3540316620

ISBN-13: 9783540316626

This quantity provides the lawsuits of the ninth on-line global convention on gentle Computing in commercial purposes, hung on the area huge net in 2004. It comprises lectures, unique papers and tutorials provided in the course of the convention. The ebook brings jointly amazing study and advancements in smooth computing, together with evolutionary computation, fuzzy common sense, neural networks, and their fusion, and its purposes in technological know-how 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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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. The decision regions of a single-layer network are bound by hyperplanes whereas those of two-layer networks may have open or closed convex decision regions (Haykin 1994, Lippman 1987). One can select the model depending upon the application domain. The multilayer network is a very popular artificial neural network architecture and has performed well in a variety of applications in several domains including classification from radiographic testing (Liao and Tang 1997, Stepinski and Lingvall 2000).

E. if the processor is Master Processor, it distributes the available number of units among other processors, based on the size of Kohonen layer and total number of processors available. It also sends the position of the starting neuron in the two dimensional lattice of the Kohonen layer to each processor, as absolute position of a neuron is required while calculating the lateral distance between winning neuron and excited neuron in the topological neighborhood function. Parallel Neuro Classifier for Weld Defect Classification 43 Step 4: From a limit file, the (a) Maximum and minimum values of each feature among all the examples and (b) Percentage of each class in the whole data set are read.

Triantaphyllou, Chang, P. C. PDF Lippman, R. P. (1987), “An introduction to computing with neural nets”, IEEE ASSP Magazine, 4, 6-22. Nafaa, N. Redouane, D. and Amar, B. htm Rao, B. P. , Raj, B. and Kalyansundaram, P. (2002), “An artificial neural networks for eddy current testing of austenitic stainless steel welds”, NDT & E International, Vol. 35, pp: 393-398 Reich, Y. (1997), Machine learning techniques for civil engineering problems, Microcomputers in Civil Engineering, 12, 4, 307-322. Reich, Y.

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Applied Soft Computing Technologies: The Challenge of Complexity (Advances in Soft Computing) by Ajith Abraham

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