Titlebook: Automatic Learning Techniques in Power Systems; Louis A. Wehenkel Book 1998 Springer Science+Business Media New York 1998 Counter.Soft Com

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Future Orientations. We start by describing probabilistic security assessment and its connection with the automatic learning framework. Then we discuss the need for enhancing automatic learning methods in order to exploit more efficiently temporal data. We conclude by proposing a software architecture which should be

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2196-3185 s, computer science, artificial intelligence, biologyand psychology. Its applications to engineering problems, such asthose encountered in electrical power systems, are thereforechallenging, while extremely promising. More and more data have becomeavailable, collected from the field by systematic ar

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Paulo Freire and Adult Education, to evaluate the approach on their systems. The second objective is to provide further illustrations of the type of results which may be obtained by a sample of the automatic learning methods described in the first part of the book.

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Policies for Adult Learning in Scotlandproblems which are already solved or are in the process of being solved in the near future. Clearly, many engineers in Utilities will be skeptical and reluctant in using any new technology without strong motivations, especially as technical concerns tend to become secondary with respect to more commercial ones.

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https://doi.org/10.1057/9781137388254ncing automatic learning methods in order to exploit more efficiently temporal data. We conclude by proposing a software architecture which should be developed in order to enable the systematic use of the approach.

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Statistical Methodseristics of these methods is that they handle input information in the form of numerical attributes. Thus all non-numerical information must be translated into numbers via an appropriate coding scheme. In the case of power system problems, this concerns mainly the topological information, which is assumed to be coded by binary 0/1 indicators.

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