Application of Gray generalised codes in the process of collecting learning vectors of artificial neural networks for the purpose of automatic filter tuning

This elaboration shows that application of Gray generalised codes for determining the position of tuning elements in the process of collecting learning vectors from the filter with the use of a one-arm robot is an alternative for random detuning in the process of customizing the algorithm for microwave filter tuning. Numerical simulations which were performed prove that the method presented here is optimal, considering the minimum number of changes in the arm of the robot (SCARA - one-arm robot) and total angular changes of tuning elements.