DATA DENOISING PROCEDURE FOR NEURAL NETWORK PERFORMANCE IMPROVEMENT

This paper will present training data denoising procedure for neural network performance improvement. Performance improvement will be measured by evaluation criterion which is based on a training estimation error and signal strength factor. Strength factor will be obtained by applying denoising method on a default training signal. The method is based on a noise removal procedure performed on the original signal in a manner which is defined by the proposed algorithm. Ten different processed signals are obtained from the performed method on a default noisy signal. Those signals are then used as a training data for the nonlinear autoregressive neural network learning phase. Empirical comparisons are made at the end, and they show that the proposed denoising procedure is an effective way to improve network performances when the training set possesses the significant noise component

LABORATORY CNC MACHINE FOR EDUCATION OF STUDENTS IN CONTROL SYSTEMS ENGINEERING

Modern states seek to build a society based on knowledge, and in this sense, the IPA project ADRIA HUB aims to connect students, universities and companies into a single entity in which each party realizes many benefits. Practical part of this project consists of the pilot projects related to the improvements in the woodworking industry. For the purpose of the project realization, a specific laboratory CNC machine was designed and implemented by the Laboratory for Modeling, Simulation and Control Systems at the Faculty of Electronic Engineering in Niš. CNC machine, presented in this paper, is now actively used in laboratory work. Students have the opportunity to gain practical knowledge and master the techniques of controlling this machine as part of their studies in Control Systems Engineering