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OverviewAutomatic learning is a complex, multidisciplinary field of research and development, involving theoretical and applied methods from statistics, computer science, artificial intelligence, biology and psychology. Its applications to engineering problems, such as those encountered in electrical power systems, are therefore challenging, while extremely promising. More and more data has become available, collected from the field by systematic archiving, or generated through computer-based simulation. To handle this increase in data, automatic learning can be used to provide systematic approaches, without which the increasing data amounts and computer power would be of little use. This text is dedicated to the practical application of automatic learning to power systems. Power systems to which automatic learning can be applied are screened and the complementary aspects of automatic learning, with respect to analytical methods and numerical simulation, are investigated. The book presents a representative subset of automatic learning methods - basic and more sophisticated ones - available from statistics (both classical and modern), and from artificial intelligence (both hard and soft computing). The text also discusses appropriate methodologies for combining these methods to make the best use of available data in the context of real-life problems. It should be a useful reference source for professionals and researchers developing automatic learning systems in the electrical power field. Full Product DetailsAuthor: Louis A. WehenkelPublisher: Springer Imprint: Springer Edition: 1998 ed. Volume: v. 429 Dimensions: Width: 15.50cm , Height: 1.90cm , Length: 23.50cm Weight: 1.370kg ISBN: 9780792380689ISBN 10: 0792380681 Pages: 280 Publication Date: 30 November 1997 Audience: College/higher education , Professional and scholarly , Postgraduate, Research & Scholarly , Professional & Vocational Format: Hardback Publisher's Status: Active Availability: In Print  This item will be ordered in for you from one of our suppliers. Upon receipt, we will promptly dispatch it out to you. For in store availability, please contact us. Table of Contents1. Introduction.- 1.1 Historical perspective on automatic learning.- 1.2 An automatic learning tool-box.- I Automatic Learning Methods.- 2. Automatic Learning is Searching a Model Space.- 3. Statistical Methods.- 4. Artificial Neural Networks.- 5. Machine Learning.- 6. Auxiliary Tools and Hybrid Techniques.- II Application of Automatic Learning to Security Assessment.- 7. Framework for Applying Automatic Learning to DSA.- 8. Overview of Security Problems.- 9. Security Information Data Bases.- 10. A Sample of Real-Life Applications.- 11. Added Value of Automatic Learning.- 12. Future Orientations.- III Automatic Learning Applications in Power Systems.- 13. Overview of Applications by Type.- References.ReviewsAuthor InformationTab Content 6Author Website:Countries AvailableAll regions |
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