Ebook: Neural Network Engineering in Dynamic Control Systems

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.

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Content:
Front Matter....Pages i-xiii
Neural Approximation: A Control Perspective....Pages 1-25
Dynamic Systems in Neural Networks....Pages 27-41
Adaptive Neurocontrol of a Certain Class of MIMO Discrete-Time Processes Based on Stability Theory....Pages 43-60
Local Model Architectures for Nonlinear Modelling and Control....Pages 61-82
On ASMOD — An Algorithm for Empirical Modelling using Spline Functions....Pages 83-104
Semi-Empirical Modeling of Non-Linear Dynamic Systems through Identification of Operating Regimes and Local Models....Pages 105-126
On Interpolating Memories for Learning Control....Pages 127-152
Construction and Design of Parsimonious Neurofuzzy Systems....Pages 153-177
Fast Gradient Based Off-Line Training of Multilayer Perceptrons....Pages 179-200
Kohonen Network as a Classifier and Predictor for the Qualification of Metal-Oxide-Surfaces....Pages 201-220
Analysis and Classification of Energy Requirement Situations Using Kohonen Feature Maps within a Forecasting System....Pages 221-237
A Radial Basis Function Network Model for the Adaptive Control of Drying Oven Temperature....Pages 239-254
Hierarchical Competitive Net Architecture....Pages 255-275
Back Matter....Pages 277-278

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Content:
Front Matter....Pages i-xiii
Neural Approximation: A Control Perspective....Pages 1-25
Dynamic Systems in Neural Networks....Pages 27-41
Adaptive Neurocontrol of a Certain Class of MIMO Discrete-Time Processes Based on Stability Theory....Pages 43-60
Local Model Architectures for Nonlinear Modelling and Control....Pages 61-82
On ASMOD — An Algorithm for Empirical Modelling using Spline Functions....Pages 83-104
Semi-Empirical Modeling of Non-Linear Dynamic Systems through Identification of Operating Regimes and Local Models....Pages 105-126
On Interpolating Memories for Learning Control....Pages 127-152
Construction and Design of Parsimonious Neurofuzzy Systems....Pages 153-177
Fast Gradient Based Off-Line Training of Multilayer Perceptrons....Pages 179-200
Kohonen Network as a Classifier and Predictor for the Qualification of Metal-Oxide-Surfaces....Pages 201-220
Analysis and Classification of Energy Requirement Situations Using Kohonen Feature Maps within a Forecasting System....Pages 221-237
A Radial Basis Function Network Model for the Adaptive Control of Drying Oven Temperature....Pages 239-254
Hierarchical Competitive Net Architecture....Pages 255-275
Back Matter....Pages 277-278
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