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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Overview of Applications by TypeThe numerous illustrations given in Parts I and II aimed at showing many alternative ways to exploit data by automatic learning. In particular, the tool-box approach came to us as the natural way to exploit the large amounts of data within our large-scale applications to power system dynamic security assessment.

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2196-3185 ive subset of automatic learningmethods - basic and more sophisticated ones - availablefrom statistics (both classical and modern), and from artificialintellige978-1-4613-7489-3978-1-4615-5451-6Series ISSN 2196-3185 Series E-ISSN 2196-3193

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Introductionic information (knowledge) from data bases containing large amounts of low level data. The researchers in the field are statisticians and computer scientists, but also psychologists and neuro-physiologists. The latter’s aim is mainly to understand human learning abilities and they use automatic lear

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Framework for Applying Automatic Learning to Dynamic Security Assessmentific way. Although there are many different more or less sophisticated tools, the most widely accepted one is numerical simulation. In planning or operations planning departments, engineers thus use numerical simulation and their own expertise to run some scenarios and extract, by hand, the relevant

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