Neural Network Engineering in Dynamic Control Systems

1. Front Matter

   Pages i-xiii

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2. Neural Approximation: A Control Perspective

   • Rafał Żbikowski, Andrzej Dzieliński

   Pages 1-25

3. Dynamic Systems in Neural Networks

   • Kevin Warwick, Chandrasekhar Kambhampati, Patrick Parks, Julian Mason

   Pages 27-41

4. Adaptive Neurocontrol of a Certain Class of MIMO Discrete-Time Processes Based on Stability Theory

   • Jean-Michel Renders, Marco Saerens, Hugues Bersini

   Pages 43-60

5. Local Model Architectures for Nonlinear Modelling and Control

   • Roderick Murray-Smith, Kenneth Hunt

   Pages 61-82

6. On ASMOD — An Algorithm for Empirical Modelling using Spline Functions

   • Tom Kavli, Erik Weyer

   Pages 83-104

7. Semi-Empirical Modeling of Non-Linear Dynamic Systems through Identification of Operating Regimes and Local Models

   • Tor A. Johansen, Bjarne A. Foss

   Pages 105-126

8. On Interpolating Memories for Learning Control

   • H. Tolle, S. Gehlen, M. Schmitt

   Pages 127-152

9. Construction and Design of Parsimonious Neurofuzzy Systems

   • K. M. Bossley, D. J. Mills, M. Brown, C. J. Harris

   Pages 153-177

10. Fast Gradient Based Off-Line Training of Multilayer Perceptrons

    • Seán McLoone, George Irwin

    Pages 179-200

11. Kohonen Network as a Classifier and Predictor for the Qualification of Metal-Oxide-Surfaces

    • Waltraud Kessler, Rudolf W. Kessler

    Pages 201-220

12. Analysis and Classification of Energy Requirement Situations Using Kohonen Feature Maps within a Forecasting System

    • Steffen Heine, Ingo Neumann

    Pages 221-237

13. A Radial Basis Function Network Model for the Adaptive Control of Drying Oven Temperature

    • Olivier Dubois, Jean-Louis Nicolas, Alain Billat

    Pages 239-254

14. Hierarchical Competitive Net Architecture

    • Theresa W. Long, Emil L. Hanzevack

    Pages 255-275

15. Back Matter

    Pages 277-278

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The series Advances in Industrial Control aims to report and encourage technology transfer in control engineering. The rapid development of control technology impacts all areas of the control discipline. New theory, new controllers, actuators, sensors, new industrial processes, computer methods, new applications, new philosophies, .... , new challenges. Much of this development work resides in industrial reports, feasibility study papers and the reports of advanced collaborative projects. The series offers an opportunity for researchers to present an extended exposition of such new work in all aspects of industrial control for wider and rapid dissemination. Within the control community there has been much discussion of and interest in the new Emerging Technologies and Methods. Neural networks along with Fuzzy Logic and Expert Systems is an emerging methodology which has the potential to contribute to the development of intelligent control technologies. This volume of some thirteen chapters edited by Kenneth Hunt, George Irwin and Kevin Warwick makes a useful contribution to the literature of neural network methods and applications. The chapters are arranged systematically progressing from theoretical foundations, through the training aspects of neural nets and concluding with four chapters of applications. The applications include problems as diverse as oven tempera­ ture control, and energy/load forecasting routines. We hope this interesting but balanced mix of material appeals to a wide range of readers from the theoretician to the industrial applications engineer.

• algorithms
• architecture
• control
• control system
• control systems
• learning systems
• model
• modeling
• neural networks

• Daimler-Benz AG, Berlin, Germany

  Kenneth J. Hunt

• Department of Electrical and Electronic Engineering, Queen’s University of Belfast, Belfast, UK

  George R. Irwin

• Department of Cybernetics, School of Engineering and Information Sciences, University of Reading, Reading, UK

  Kevin Warwick

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