The paper describes an application of regularization techniques to an automatic choice of parameters driving the learning process in the NM-Delta neural network architecture. The heterogeneous learning algorithm is identified as very similar to the Levenberg-Marquardt method but with a considerably smaller computational cost and different justification of parameter selection. The performance of the modified algorithm proves to be comparable with that of the Levenberg-Marquardt.
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The past few years have witnessed a dynamic growth variety of neural network applications. Range of these applications is very wide especially in industrial process control. As in nature, the neural network is determined by the connections between the elements, and we can train its to perform the particular function by adjusting special values (weights between elements) [1,4,7]. This paper presents DC motor model controlled by neural network Proportional-Derivate controller in comparison with classic PD controller. The research concern different network architectures and training functions. The models are presented and the experimental results signals are shown using graphical charts.
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