Feedback Control Based on Neural Networks.

The paper describes an algorithm for the synthesis of neural networks to control gyrostabilizer. The neural network performs the role of a state vector observer. The role of such an observer is to provide feedback on gyrostabilizer, which is illustrated in the article. The paper details the issue of specific stage-related peculiarities of classic algorithms: choosing the network architecture, learning the neural network and verifying the results of feedback control. The article presents an optimal configuration of the neural network like memory depth, number of layers and neurons in these layers, and activation layer functions. It also provides data on dynamic systems to improve learning neural network learning. There is also provided an optimal training pattern. [ABSTRACT FROM AUTHOR]