Download Artificial Intelligence and Soft Computing -- Icaisc 2004 by Rutkowski L. (Ed), Zadeh L. A. (Ed) PDF

By Rutkowski L. (Ed), Zadeh L. A. (Ed)

This ebook constitutes the refereed court cases of the seventh overseas convention on synthetic Intelligence and delicate Computing, ICAISC 2004, held in Zakopane, Poland in June 2004.The 172 revised contributed papers provided including 17 invited papers have been conscientiously reviewed and chosen from 250 submissions. The papers are prepared in topical sections on neural networks, fuzzy structures, evolutionary algorithms, tough units, smooth computing in class, picture processing, robotics, multiagent structures, difficulties in AI, clever regulate, modeling and process identity, clinical purposes, mechanical purposes, and functions in a variety of fields.

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References 1. , Piuri. V. Designing and Rightsizing the Information System Architecture. To appear on Information Systems Frontiers, Kluwer Academic Publishers. 2. , Trubian, M. 2004. A Cost-oriented Approach for Inftrastructural Design. SAC 2004 Proceedings. Nicosia, Cyprus. 3. Ein-Dor, P. 1985. Grosch’s Law Revisited: CPU Power and the Cost of Computing. Management Communication of the ACM. 28(2), 142-150. 4. , Pirkul, H. 1986. Computer and Database Location in Distributed Computer Systems. IEEE Transaction on Computers.

The nonparametic decision problem is then the following: For the given and one sholud determine The determination of may be decomposed into two steps. In the first step, one should find the distribution satisfying the equation and in the second step, one should determine the distribution the equation satisfying where The distribution may be considered as a knowledge of the decision making (KD) or an uncertain decision algorithm. The deterministic decision algorithm may be obtained via a determinization of the uncertain algorithm, which may consist in using the mean value: It is worth noting that the anologous problem may be considered for the nonparametric descriptions of the uncertainty using random or fuzzy variables.

The remote execution reduces management costs while increases the computing capacity of the remote server and hence investment costs. If a class of users uses a set o applications that can be executed indifferently by the HFC or by the remote server then configurations have to be evaluated. The computing capacity of PCs is obtained as the maximum value of MIPS required by locally executed applications. Similarly, computing capacity of thin/HFC servers (servers supporting thin and HFC clients) is evaluated as the maximum value of MIPS required by applications that are executed remotely, considering the number of concurrent users of the corresponding user class ([5], [1], [2]).

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