Generating Software

UT Arlington

Software for Generating Training Data Files

FM Demodulation

This software can be used to generate training data files that can be used to train a neural network to perform demodulation of an FM (Frequency Modulation) signal containing a random waveform.

The generated training data file will have the user specified number of input samples which are nothing but values of the FM waveform taken over the specified window size and one output corresponding to the window center sample of the original random waveform. The window size is an odd number and a minimum of 3.

The user can generate any number of patterns and save it as desired. Both training and testing files can be generated for the FM Demodulation application.

Source Code (zipped)
Training Files (zipped)
Sample Testing (zipped)

Matrix Inversion

This software can be used to generate training data files that can be used to train a neural network to perform matrix inversion.

The generated training data file will have four input samples which are nothing but elements of a random 2x2 matrix and four output samples which are the elements of the corresponding inverse matrix.

The user can generate any number of patterns and save it as desired. Both training and testing files can be generated for the Matrix Inversion application.

Source Code (WinZipped version)
Training Files (WinZipped version)
Sample Testing (WinZipped version)

Exponential Estimation

This software can be used to generate training data files that can be used to train a neural network to perform estimation of parameters of an exponential waveform, from its KLT coefficients.

The exponential waveform is generated as A*exp(-n/T)+n(n), where A and T have uniform densities between (1,4) and (10,50) respectively and n(n) is WGN with standard deviation of 0.1.

Each pattern of the training data file consists of 2 KLT coefficients of the exponential waveform as inputs and the corresponding values of A and T as the outputs. The program also generates the Cramer-Rao bounds on the training error.

Source Code (WinZipped version)
Training Files (WinZipped version)
Sample Testing (WinZipped version)