Neural Network Engineering in Dynamic Control Systems: Advances in Industrial Control

Overview

This study evaluates the state of the art in the area of neural networks from the engineering perspective. The contributions examine ways of improving the engineering involved in neural network modelling and control, so that the theoretical power of learning systems can be harnessed for practical applications. Neural Network Engineering in Dynamic Control Systems seeks to provide answers to the following questions:
• Which network architecture ...

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Overview

This study evaluates the state of the art in the area of neural networks from the engineering perspective. The contributions examine ways of improving the engineering involved in neural network modelling and control, so that the theoretical power of learning systems can be harnessed for practical applications. Neural Network Engineering in Dynamic Control Systems seeks to provide answers to the following questions:
• Which network architecture for which application?
• Can constructive learning algorithms capture the underlying dynamics while avoiding overfitting?
• How can we introduce a priori knowledge or models into neural networks?
• Can experimental design and active learning be used automatically to create "optimal" training sets?
• How can we validate a neural network model?

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Product Details

Table of Contents

1 Neural Approximation: A Control Perspective 1
2 Dynamic Systems in Neural Networks 27
3 Adaptive Neurocontrol of a Certain Class of MIMO Discrete-time Processes Based on Stability Theory 43
4 Local Model Architectures for Nonlinear Modelling and Control 61
5 On ASMOD - An Algorithm for Empirical Modelling using Spline Functions 83
6 Semi-Empirical Modeling of Non-linear Dynamic Systems through Identification of Operating Regimes and Local Models 105
7 On Interpolating Memories for Learning Control 127
8 Construction and Design of Parsimonious Neurofuzzy Systems 153
9 Fast Gradient Based Off-line Training of Multilayer Perceptrons 179
10 Kohonen Network as a Classifier and Predictor for the Qualification of Metal-Oxide Surfaces 201
11 Analysis and Classification of Energy Requirement Situations Using Kohonen Feature Maps within a Forecasting System 221
12 A Radial Basis Function Network Model for Adaptive Control of Drying Oven Temperature 239
13 Hierarchical Competitive Net Architecture 255
Index 277
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