IPNNL Software

IPNNL produces neural net related software, provides several software development services like customization of commercial software and new source code development for various fields. This page gives you an introduction to work we have done and our products.

IPNNL is also a member of the Arlington Technology Incubator.

Performance of commercially available software like Matlab and SNNS is compared to IPNNL software here. For example source code click here.

Research Software Products

Product

Description

 

MOLF-ADAPT-VERSION2(MATLAB)
Added on  11/14/2016 

Software for Designing Multilayer Perceptron Classification Networks
Better TESTING performance than MLF-ADAPT and MOLF-ADAPT-MERGE.

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Read Me

DEEP LEARNING(MATLAB)
Added on   11/14/2016 

Software for Designing Deep Learning Networks

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Read Me

MOLF-ADAPT (MATLAB version)
Compiled  08/24/2015

See related publications

Software for Designing Multilayer Perceptron Regression and Classification Networks

Download
Read Me
Screen Shot

MOLF-ADAPT related papers:
  • Rohit Rawat, Jignesh Patel and Michael Manry, "Minimizing Validation Error With Respect to Network Size and Number of Training Epochs," the 2013 International Joint Conference on Neural Networks.
  • M.T. Manry, H. Chandrasekaran, and C-H Hsieh, "Signal Processing Applications of the Multilayer Perceptron," book chapter in Handbook on Neural Network Signal Processing, edited by Yu Hen Hu and Jenq-Nenq Hwang, CRC Press, 2001.
  • Changhua Yu, Michael T. Manry, and Jiang Li, “An Efficient Hidden Layer Training Method for Multilayer Perceptron”, NeuroComputing, vol. 70, January 2007, pp. 29 53.
  • P. L. Narasimha, W.H. Delashmit, M.T. Manry, Jiang Li, and F. Maldonado, “An Integrated Growing-Pruning Method for Feedforward Network Training,” NeuroComputing, vol. 71, Spring 2008, pp. 2831-2847.

MOLF-ADAPT-MERGE (MATLAB version)
Compiled  10/24/2015 (fix added)

See related publications

Software for Designing Multilayer Perceptron Regression and Classification Networks

Download
Read Me
Screen Shot

MOLF-ADAPT-MERGE related papers:
  • "A Novel Method of K – Fold Cross Testing and Validation and Model Selection" Nayana P Thatren & Rohit Rawat
  • Rohit Rawat, Jignesh Patel and Michael Manry, "Minimizing Validation Error With Respect to Network Size and Number of Training Epochs," the 2013 International Joint Conference on Neural Networks.
  • M.T. Manry, H. Chandrasekaran, and C-H Hsieh, "Signal Processing Applications of the Multilayer Perceptron," book chapter in Handbook on Neural Network Signal Processing, edited by Yu Hen Hu and Jenq-Nenq Hwang, CRC Press, 2001.

MOLF (Executable)
Compiled 02/09/2013

See related publications

Multiple Optimal Learning Factors

Download
(Password: INITIALIZE)

If MSVCR100.DLL is missing on your machine, also install: MSVC Runtime

MOLF related papers:

RBF-MKM (MATLAB version)
Compiled  08/26/2015

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Software for Designing Radial Basis Function Networks

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Read Me
Screen Shot

MOLF-ADAPT related papers:
  • "TRAINING ALGORITHM FOR MULTI-CLASS RADIAL BASIS FUNCTION CLASSIFIERS" by Yilong Hao
  • Rohit Rawat, Jignesh Patel and Michael Manry, "Minimizing Validation Error With Respect to Network Size and Number of Training Epochs," the 2013 International Joint Conference on Neural Networks.
  • Tyagi, Kanishka. "Second Order Training Algorithms For Radial Basis Function Neural Networks." (2012).

OWO-NEWTON
Compiled  10/27/2015

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Software for Designing Multilayer Perceptron Regression Networks

Download
Read Me
Screen Shot

CG
Compiled 11/12/2012

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Conjugate Gradient

Download
(Password: INITIALIZE)

If MSVCR100.DLL is missing on your machine, also install: MSVC Runtime

CG related papers:

BP
Compiled 11/23/2012

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Backpropagation

Download
(Password: INITIALIZE)

If MSVCR100.DLL is missing on your machine, also install: MSVC Runtime

BP related papers:

MOL_ADAPT-BP
Compiled 11/23/2012

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Backpropagation

Download
(Password: INITIALIZE)

If MSVCR100.DLL is missing on your machine, also install: MSVC Runtime

Freeware - with source code

MLP-MAP
(FREEWARE)

Basic Multilayer Perceptron for Regression/Regression Analysis
(Updated 02/23/2012)
Details

Download
(Password: INITIALIZE)

MLP-CLASS
(FREEWARE)

Basic Multilayer Perceptron for Designing Classification Networks
(Updated 03/27/2010)
Details

Download
(Password: INITIALIZE)

MLP-MAP and MLP-CLASS MATLAB Version
(FREEWARE)

Basic Multilayer Perceptron for Regression or Classification Analysis using the OWO-BP algorithm.

Readme Screenshot

Download

Statistical Evaluation Tool for Classifiers (MATLAB)
(FREEWARE)

This tool accepts processing results files generated by various classifiers, and performs sensitivity, specificity, ROC curve, and AUC computation. It is capable of generating and averaging multiple ROC curves when cross-testing.
Details

Download

License Plate Recognition Utilities

 
Plate Database Generator

This tools reads vehicle images and allows a human user to click on the four corners of the license plate and save it to a file.

Download

Character database generator

This tools reads cropped license plate images and applies a segmentaion algorithm to it to extract individual characters and the state logo.

Coming soon

 
Plate Finder Demo

Automatic detection of the license plate from vehicle images.

Coming soon.

Student Projects

Dependency Detector Software  
(EXE)

Dependency Detector is a Graphical User Interface Software which is used to detect and eliminate the Linearly dependent Inputs from the Neural Network Input data file.

  Download

Bayes Gaussian Classifier Software  
(EXE)

Bayes Gaussian Classifier is a Graphical User Interface used for classification of patterns using the Bayes rule. The training and testing errors can be calculated simultaneously.

  Download

Mail Segmentation Software  
(EXE)

Mail segmentation is a Graphical User Interface Software which is used to identify and segment various regions(stamp,address,advertisements etc.) on mail image.This segmented image can further be used for various applications like mail sorting,zipcode identification,stamp verification etc.

  Download

Legacy Software

Product

Description

 

NuMap7.1
(Upgradeable)
Compiled  9/25/2007

See related publications

Software for Designing Nonlinear Regression and Classification Networks
(neural and conventional)  
It is MUCH faster than the Matlab NN toolbox. Details    Versions and Limitations

Download Basic Version
Read Me
.PAD File
Screen Shot

NuMap7.1 related papers:
  • M.T. Manry, H. Chandrasekaran, and C-H Hsieh, "Signal Processing Applications of the Multilayer Perceptron," book chapter in Handbook on Neural Network Signal Processing, edited by Yu Hen Hu and Jenq-Nenq Hwang, CRC Press, 2001.
  • Changhua Yu, Michael T. Manry, and Jiang Li, “An Efficient Hidden Layer Training Method for Multilayer Perceptron”, NeuroComputing, vol. 70, January 2007, pp. 29 53.
  • H. Chandrasekaran, Jiang Li, W.H. Delashmit, Pramod Narasimha, Changhua Yu and Michael T. Manry, “Convergent Design of Piecewise Linear Neural Networks”, NeuroComputing, vol. 70, October 2006, pp. 1022-1039.
  • Jiang Li, Michael T. Manry, Pramod Narasimha, and Changhua Yu, “Feature Selection Using a Piecewise Linear Network”, IEEE Trans. on Neural Networks, Vol. 17, no. 5, September 2006, pp. 1101-1115.
  • P. L. Narasimha, W.H. Delashmit, M.T. Manry, Jiang Li, and F. Maldonado, “An Integrated Growing-Pruning Method for Feedforward Network Training,” NeuroComputing, vol. 71, Spring 2008, pp. 2831-2847.

NuClass7.1
(Upgradeable)
Compiled  9/25/2007
Last updated on 6/26/2012

See related publications

Software for Designing Nonlinear Classification Networks
(neural and conventional)  
It is MUCH faster than the Matlab NN toolbox. Details    Versions and Limitations

Download Basic Version
Read Me
.PAD File  
Screen Shot

NuClass7.1 related papers:
  • M.T. Manry, H. Chandrasekaran, and C-H Hsieh, "Signal Processing Applications of the Multilayer Perceptron," book chapter in Handbook on Neural Network Signal Processing, edited by Yu Hen Hu and Jenq-Nenq Hwang, CRC Press, 2001.
  • R.G. Gore, Jiang Li, Michael T. Manry, Li-Min Liu, Changhua Yu, and John Wei, "Iterative Design of Neural Network Classifiers through Regression". International Journal on Artificial Intelligence Tools, Vol. 14, Nos. 1&2 (2005) pp. 281-301.
  • Changhua Yu, Michael T. Manry, and Jiang Li, “An Efficient Hidden Layer Training Method for Multilayer Perceptron”, NeuroComputing, vol. 70, January 2007, pp. 29 53.
  • H. Chandrasekaran, Jiang Li, W.H. Delashmit, Pramod Narasimha, Changhua Yu and Michael T. Manry, “Convergent Design of Piecewise Linear Neural Networks”, NeuroComputing, vol. 70, October 2006, pp. 1022-1039.
  • Jiang Li, Jianhua Yao, Ronald M. Summers, Nicholas Petrick, Michael T. Manry, and Amy K. Hara, “An Efficient Feature Selection Algorithm for Computer-Aided Polyp Detection,” special issue of the International Journal on Artificial Intelligence Tools (IJAIT), vol. 15, no. 6, December 2006, pp. 893-915.
  • Jiang Li, M.T. Manry, Randall Wilson, and Changhua Yu "Prototype Based Classifier Design with Pruning". International Journal on Artificial Intelligence Tools, 2005.
  • P. L. Narasimha, W.H. Delashmit, M.T. Manry, Jiang Li, and F. Maldonado, “An Integrated Growing-Pruning Method for Feedforward Network Training,” NeuroComputing, vol. 71, Spring 2008, pp. 2831-2847.

GIGO
(Upgradable)
Compiled 12/31/2007

See related publications

Software for Troubleshooting Neural Network Text-type Training Data Files

Download
(Password: troubleshoot)

GIGO related papers:
  • Jiang Li, Michael T. Manry, Pramod Narasimha, and Changhua Yu, “Feature Selection Using a Piecewise Linear Network”, IEEE Trans. on Neural Networks, Vol. 17, no. 5, September 2006, pp. 1101-1115.

NuMap/NuClass Version Number Information


Version

New Features

7.00

First Windows 2000 version

7.01

Improved GUI and Help. First version to use DLLs.

7.02

Additional Networks, improved utilities GUI and Installation

7.03

More efficient MLP Training, additional utilities.

7.04

Greatly improved GUI and Help

7.05

Validation option included for Pruning
(Available now  !)

7.06

Improved help, fast MLP validation, improved feature selection options (Coming Soon)

© 2010 The University of Texas at Arlington
© 2010 Image Processing and Neural Networks Lab