Applying regression models to calculate the Q factor of multiplexed video signal based on Optisystem

The objective is to analyze the input parameters of Dense Wavelength Multiplexing System and accurately predict the output parameters, using machine learning techniques and model its dependencies on the input parameters such as Frequency, Frequency Spacing, Bit Rate and Fiber length. The training data will be mined from Optisystem 13.0 software and machine learning algorithms will be implemented using R and MATLAB. The algorithms used are Multivariable regression models and neural networks. The accuracy of the two methods are compared. The predicted values have a close co-relation with input parameters and cost function errors have been minimized making use of these techniques.