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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