Artificial Neural Networks [electronic resource] : An Introduction to ANN Theory and Practice / edited by P. J. Braspenning, F. Thuijsman, A. J. M. M. Weijters.

By: Braspenning, P. J [editor.]Contributor(s): Thuijsman, F [editor.] | Weijters, A. J. M. M [editor.] | SpringerLink (Online service)Material type: TextTextLanguage: English Series: Lecture Notes in Computer Science: 931Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 1995Description: IX, 299 p. online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9783540492832Subject(s): Computer science | Artificial intelligence | Optical pattern recognition | Systems theory | Numerical analysis | Combinatorics | Computer Science | Artificial Intelligence (incl. Robotics) | Computation by Abstract Devices | Numerical Analysis | Pattern Recognition | Combinatorics | Systems Theory, ControlAdditional physical formats: Printed edition:: No titleDDC classification: 006.3 LOC classification: Q334-342TJ210.2-211.495Online resources: Click here to access online
Contents:
Introduction: Neural networks as associative devices -- Backpropagation networks for Grapheme-Phoneme conversion: A non-technical introduction -- Back Propagation -- Perceptrons -- Kohonen network -- Adaptive Resonance Theory -- Boltzmann Machines -- Representation issues in Boltzmann machines -- Optimisation networks -- Local search in combinatorial optimization -- Process identification and control -- Learning controllers using neural networks -- Key issues for successful industrial neural-network applications: An application in geology -- Neural cognodynamics -- Choosing and using a neural net.
In: Springer eBooksSummary: This book presents carefully revised versions of tutorial lectures given during a School on Artificial Neural Networks for the industrial world held at the University of Limburg in Maastricht, Belgium. The major ANN architectures are discussed to show their powerful possibilities for empirical data analysis, particularly in situations where other methods seem to fail. Theoretical insight is offered by examining the underlying mathematical principles in a detailed, yet clear and illuminating way. Practical experience is provided by discussing several real-world applications in such areas as control, optimization, pattern recognition, software engineering, robotics, operations research, and CAM.
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Introduction: Neural networks as associative devices -- Backpropagation networks for Grapheme-Phoneme conversion: A non-technical introduction -- Back Propagation -- Perceptrons -- Kohonen network -- Adaptive Resonance Theory -- Boltzmann Machines -- Representation issues in Boltzmann machines -- Optimisation networks -- Local search in combinatorial optimization -- Process identification and control -- Learning controllers using neural networks -- Key issues for successful industrial neural-network applications: An application in geology -- Neural cognodynamics -- Choosing and using a neural net.

This book presents carefully revised versions of tutorial lectures given during a School on Artificial Neural Networks for the industrial world held at the University of Limburg in Maastricht, Belgium. The major ANN architectures are discussed to show their powerful possibilities for empirical data analysis, particularly in situations where other methods seem to fail. Theoretical insight is offered by examining the underlying mathematical principles in a detailed, yet clear and illuminating way. Practical experience is provided by discussing several real-world applications in such areas as control, optimization, pattern recognition, software engineering, robotics, operations research, and CAM.

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