NONLINEAR NETWORKS FOR REGRESSION/APPROXIMATION:
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This is a readme file for training data files included in the NuMap 7.04 package.

Location of Training files:
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A few training files are included in the NuMap 7.04 package. It can be found in the folder "Training Files", under the NuMap7 directory. These training data files are already in the standard format and are ready to be used with NuMap 7.04.

Obtaining new Training Files:
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The Image Processing and Neural Networks Lab (IPNNL) website has free source codes that can be used to generate training files. Use them to generate training files with desired patterns, inputs and outputs. Available at:

	http://www-ee.uta.edu/eeweb/IP/training_data_files.htm

Below is a fast reference table for the training files provided with NuMap7 folowed by a detailed description for each file.


TRAINING FILE NAME	INPUTS		OUTPUTS		PATTERNS
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1. FMTRAIN.DAT		  5		   1		 1024

2. TWOD.TRA		  8		   7		 1768

3. SINGLE2.TRA 	  	  16		   3	         10,000

4. OH7.TRA		  20		   3		 15,000

5. POW12TRN 		  12		   1		 1414

6. MAT.TRN		  4		   4		 2000


Description:
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1. FMTRAIN.DAT : ( 5 Inputs , 1 Outputs, 1024 Training Patterns, 61 KB unzipped)
 
This training file is used to train a neural network to perform demodulation of an FM (frequency modulation)         signal containing a sinusoidal message. The data are generated from the equation 

	r(n) =  Camp * cos[2* PI* n* Cfreq + Mamp *sin(2* PI* n* Mfreq )] 

where Camp = Carrier Amplitude, Mamp = Message Amplitude, Cfreq = normalized Carrier frequency,  Mfreq = 		normalized message frequency. In this data set, Camp = .5, Cfreq = .1012878, Mfreq = .01106328, and Mamp=5. 		The five inputs are r(n-2), r(n-1), r(n), r(n+1), and r(n+2). The output is Cos(2* PI* n* Mfreq ). In each 		consecutive pattern, n is incremented by 1. 

For more details,  see 

K.Rohani and M.T.Manry,"The Design of Multi-Layer Perceptrons using Building Blocks,"Proc of IJCNN 91, 		Seattle WA., pp. II-497 to II-502. 

2. TWOD.TRA : ( 8 Inputs , 7 Outputs, 1768 Training Patterns, 244 KB unzipped) 

This training file is used in the task of inverting the surface scattering parameters from an  inhomogeneous 	layer above a homogeneous half space, where both interfaces are randomly rough. The parameters to be 		inverted are the effective permittivity of the surface, the normalized rms height, the normalized surface 		correlation length, the optical depth, and single scattering albedo of an inhomogeneous irregular layer 		above a homogeneous half space from back scattering measurements. 

The training data file contains 1768 patterns. The inputs consist of  eight theoretical values of back 		scattering coefficient parameters at V and H polarization and four incident angles. The outputs were the 		corresponding values of permittivity, upper surface height, lower surface height, normalized upper surface 		correlation length, normalized lower surface correlation length, optical depth and single scattering albedo 		which had a joint uniform pdf. 

For more details,  see 

M.S.Dawson, A.K.Fung and M.T.Manry, "Surface parameter retrieval using fast learning neural networks," 		Remote Sensing Reviews, 1993, Vol. 7(1), pp. 1-18. 

M.S.Dawson, J.Olvera, A.K.Fung and M.T.Manry, "Inversion of surface parameters using fast learning neural 		networks," Proc. of IGARSS'92, Houston, Texas, May 1992, Vol II, pp 910 - 912. 

The testing version of the data file TWOD.TST is also available (Size 138K) 

This file was generated by Mike Dawson while he worked for Prof.Adrian Fung, at University of Texas at 		Arlington. Dr.Dawson currently works at Raytheon E-Systems in Garland, Texas. 

3. SINGLE2.TRA : (16 Inputs, 3 Outputs, 10,000 Training Patterns, 1.6M) 

This training data file consists of 16 inputs and 3 outputs and represents the training set for inversion of 	surface permittivity, the normalized surface rms roughness, and the surface correlation length found in back 	scattering models from randomly rough dielectric surfaces. The first 16 inputs represent the simulated back 		scattering coefficient measured at 10, 30, 50 and 70 degrees at both vertical and horizontal polarization. 		The remaining 8 are various combinations of ratios of the original eight values. These ratios correspond to  	those used in several empirical retrieval algorithms. 

For more details,  see 

A.K. Fung, Z. Li, and K.S. Chen, "Back scattering from a Randomly Rough Dielectric Surface," IEEE Trans. 		Geo. and Remote Sensing, Vol. 30, No. 2, March 1992. 

A.K. Fung, Microwave Scattering and Emission Models and Their Applications, Arctec House, 1994. 

This file was generated by Mike Dawson while he worked for Prof.Adrian Fung, at University of Texas at 		Arlington. Dr.Dawson currently works at Raytheon E-Systems in Garland, Texas. 

4. OH7.TRA : (20 Inputs, 3 Outputs, 15,000 Training Patterns, 3.1 M) 

This data set is given in Oh, Y., K. Sarabandi, and F.T. Ulaby, "An Empirical Model and an Inversion 		Technique for Radar Scattering  from Bare Soil Surfaces," in IEEE Trans. on Geoscience and Remote Sensing, 		pp. 370-381, 1992. The training set contains VV and HH polarization at L 30, 40 deg, C 10, 30, 40, 50, 60 		deg, and X 30, 40, 50 deg along with the corresponding unknowns  rms surface height, surface correlation 		length, and volumetric soil moisture content in g / cubic cm. 

5. POW12TRN : ( 12 Inputs, 1 Output, 1414 Training Patterns, 299K) 

This training file was generated using data obtained from TU Electric Company in Texas. The first ten input 		features are last ten minutes power load in megawatts for the entire TU Electric utility, which covers a 		large part of north Texas. The output is power load fifteen minutes in the future from the current time. All 	powers were originally sampled every fraction of a second, and averaged over 1 minute to reduce noise.
	
For more details, see 

K. Liu, S. Subbarayan, R.R.Shoults, M.T.Manry, C.Kwan, F.L.Lewis, and J.Naccarino, "Comparison of Very 			Short-Term Load Forecasting Techniques," IEEE Transactions on Power Systems, vol.11, no.2, May 1996, pp. 		877-882. 

M.T. Manry, R. Shoults, and J. Naccarino, "An Automated System for Developing Neural Network Short Term Load 	Forecasters," Proceedings of the 58th American Power Conference, Chicago, Ill., April 9-11, 1996, vol. 1, 		pp. 237-241. 

A testing version POW12TST (299 K) is also available for download. 


6. MAT.TRN: (4 Inputs, 4 Outputs, 2000 Training Patterns, 644K) 

This training file provides the data set for inversion of random two-by-two matrices. Each pattern consists 		of  4 input features and  4 output features. The input features, which are uniformly distributed between 0 		and 1,  represent a matrix and the four output features are elements of the corresponding inverse matrix. 		The determinants of the input matrices are constrained to be between .3 and 2.