Bernd Schürmann

University of Kaiserslautern

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Gustavo Deco

Institució Catalana de Recerca i Estudis Avançats

Alexander Köpper

University of Kaiserslautern

Karsten Berns

University of Kaiserslautern

W. Zwermann

Gesellschaft für Anlagen und Reaktorsicherheit

Joachim Altmeyer

University of Kaiserslautern

J. Hollatz

Research & Development Establishment (Engrs.)

Von Heun

Institut für Informationsverarbeitung

Dietmar Abts

Fachklinik 360°

R. Steiner

Institut für Informationsverarbeitung

Steffen Heinzl

Technical University of Applied Sciences Würzburg-Schweinfurt

Cooperative Institutions

Community Research and Development Information Service

112

Springer Nature (Germany)

104

Institut für Informationsverarbeitung

British Biophysical Society

Deutsche Nationalbibliothek

Siemens (Germany)

University of Kaiserslautern

Technical University of Munich

Goethe University Frankfurt

University of Bonn

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Spacecraft Guidance, Navigation and Control Systems

Spacecraft Guidance, Navigation and Control Systems (2000)

Bernd Schürmann

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Citations (7)

Hierarchisches Top Down Chip Planning.

Informatik-Spektrum (1988)

Bernd Schürmann

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Citations (8)

Schnittstellen und Peripheriebusse

Bernd Schürmann

Der allgemeine von Neumann-Rechner (Abbildung 3–4) kann für moderne Computersysteme nur noch als logisches Architekturschema angesehen werden. Während bis etwa Mitte der 80er Jahre noch alle Komponenten eines Computers zentral an einem einzigen Systembus angeschlossen waren, basieren heutige Rechnersysteme auf hierarchischen Bussystemen (vgl. Abbildung 3–5).

10.1007/978-3-322-86837-4_4

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Signal Processing

Lecture notes in electrical engineering (2021)

Karsten Berns Alexander Köpper Bernd Schürmann

Aliasing

Sample and hold

Analogue filter

Anti-aliasing filter

Data conversion

Analog signal processing

SIGNAL (programming language)

10.1007/978-3-030-65157-2_7

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Friction in heavy ion reactions at relativistic energies

Physics Letters B (1976)

A. Y. Abul-Magd J. Hüfner Bernd Schürmann

Fragmentation

Momentum (technical analysis)

10.1016/0370-2693(76)90741-3

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Citations (49)

The “subthreshold” production of antikaons in relativistic nuclear collisions

Physics Letters B (1984)

W. Zwermann Bernd Schürmann

Subthreshold conduction

Scattering cross-section

10.1016/0370-2693(84)90051-0

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Citations (22)

Fast fission phenomena: A possible intermediate time scale between deep inelastic reactions and compound nucleus formation

Nuclear Physics A (1981)

C. Grégoire R. Lucas C. Ngô Bernd Schürmann H. Ngô

10.1016/0375-9474(81)90645-x

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Citations (20)

Neural Learning of Chaotic System Behavior

IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences (1994)

Gustavo Deco Bernd Schürmann

Neural system

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Citations (15)

Information Processing and Coding in Spatiotemporal Dynamical Systems: Spiking Networks

Springer eBooks (2001)

Gustavo Deco Bernd Schürmann

Neural coding

Predictive coding

Information Theory

10.1007/978-1-4613-0127-1_9

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Citations (0)

Process Modelling and Control with Neural Networks: Present Status and Future Directions

Artificial Neural Nets and Genetic Algorithms (1995)

Bernd Schürmann

Even though there exist numerous traditional approaches for solving nonlinear control tasks their realization in practice often proves to be difficult, mainly because of i) insufficient analytical knowledge of the system to be controlled, and ii) because of incomplete knowledge of the physical parameters of the system. Neural networks can cope with these problems because of their capability to realize multivariate, nonlinear transformations and to learn these merely by empirical information, i.e. by representative training examples (data driven modeling). In this contribution we discuss neural methods for modeling and control and illustrate their use in real world applications. In order to improve the corresponding conventional solutions, it is crucial to i) incorporate prior knowledge (analytical and/or rule based) into the neural network and ii) to optimize its architecture by refined learning methods. We point out the future need for neural control of the complete process rather than controlling its parts independently. Further, we discuss the necessity to combine neural methods with the modern techniques of Nonlinear Dynamics for the modeling and control of processes with highly nonlinear dynamic behavior.

Realization (probability)

Process modeling

10.1007/978-3-7091-7535-4_3

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