Neural Network Engineering in Dynamic Control Systems
By: edited by Kenneth J. Hunt, George R. Irwin, Kevin Warwick
Contributors: Irwin, George R.; Warwick, Kevin
Series: Advances in Industrial Control; Advances in industrial control
Publisher: Springer London
Published: 1995
Place of publication: London
Language: eng
Format: [electronic resource] /
Number of pages: 282
ISBN-13: 9781447130680; 9781447130666
Subjects: Engineering; Computer-aided design
Description: 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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