David W. Pearson, Nigel C. Steele, Rudolf F. Albrecht

Artificial Neural Nets and Genetic Algorithms (eBook, PDF)

Proceedings of the International Conference in Alès, France, 1995

• Format: PDF

Produktbeschreibung

Artificial neural networks and genetic algorithms both are areas of research which have their origins in mathematical models constructed in order to gain understanding of important natural processes. By focussing on the process models rather than the processes themselves, significant new computational techniques have evolved which have found application in a large number of diverse fields. This diversity is reflected in the topics which are subjects of the contributions to this volume. There are contributions reporting successful applications of the technology to the solution of industrial/commercial problems. This may well reflect the maturity of the technology, notably in the sense that 'real' users of modelling/prediction techniques are prepared to accept neural networks as a valid paradigm. Theoretical issues also receive attention, notably in connection with the radial basis function neural network. Contributions in the field of genetic algorithms reflect the wide range of current applications, including, for example, portfolio selection, filter design, frequency assignment, tuning of nonlinear PID controllers. These techniques are also used extensively for combinatorial optimisation problems.

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Produktdetails

• Verlag: Springer Vienna
• Seitenzahl: 522
• Erscheinungstermin: 6. Dezember 2012
• Englisch
• ISBN-13: 9783709175354
• Artikelnr.: 53163609

Inhaltsangabe

Workshop Summary.- Invited Talk.- Plenary Session.- Session A1: Classification.- Session B1: Genetic Algorithms and Combinatorial Optimisation.- Session C1: Learning and Training.- Session A2: Applications in Biology and Biotechnology.- Session B2: Genetic and Neural Theory Combined.- Session C2: Failure Detection and Identification.- Session A3: Image, Motion and Recognition.- Session B3: Genetic Algorithms Theory.- Session C3: Neural Networks Theory.- Session A4: Image, Motion and Recognition.- Session B4: Genetic Algorithms Theory.- Session C4: Neural Networks Theory.- Session A5: Time Series, Sequences and Filters.- Session B5: Genetic Algorithms and Combinatorial Optimisation.- Session C5: Parallelism and Neural Networks.- Radial Basis Function Networks.- Session A6: Multimedia, CAD and Software Tools.- Session B6: Genetic and Neural Theory Combined.- Genetic Algorithms Theory.- Session C6: Optimisation.- Session A7: Self-Organizing Maps and Auto-Associative Memory.- Session B7: Parallelism and Genetic Algorithms.- Optimisation.- Session C7: Control Theory Applications.- Session A8: Applications in Biology and Biotechnology.- Session B8: Learning and Training.- Session C8: Mixed Applications.- Session A9: Training Data Selection.- Session B9: Parallelism and Genetic Algorithms.- Genetic Algorithms and Combinatorial Optimisation.- Session C9: Fuzzy Logic and Uncertainty.- Time Series, Sequences and Filters.