Scientific Software (SCIWARE) by Dr. Danon
WinNN

Digitize-Pro

WinNN

WinNN

Windows Neural Networks (WinNN) is a shareware windows neural networks simulator. WinNN can handle multi-layered feed forward networks and train using modified back-propagation. WinNN has a very friendly user interface and fast computational engine to run the calculations. WinNN can import data, train, and plot the results.

WinNN runs under Windows 95/98/Me/NT/2000/XP

Some of the features of WinNN:

• WinNN32 is a fast 32-bit implementation that works under Windows 95/98/NT/2000/XP.
• Smoothly trains in the background. Supports DDE with Excel (use NN predictions in Excel).
• Built in graphics to plot the NN architecture and the results.
• All files are written in simple ASCII format that can be used by other programs.
• The trained NN can easily be used from all programming languages.
• This is a mature program that is being used for since 1994.

Links and references to work done using WinNN:

• Use of Neural Networks to Determine Properties of Alkanes from their 13C-NMR Spectra
• Quantification of apoptotic and lytic cell death by video microscopy in combination with artificial neural networks, Cytometry, Volume 31, Issue 1 , Pages 20 - 28, 6 Dec 1998.
• Comparison of Viral Load and Human Leukocyte Antigen Statistical and Neural Network Predictive Models for the Rate of HIV-1 Disease Progression Across Two Cohorts of Homosexual Men. Journal of Acquired Immune Deficiency Syndromes & Human Retrovirology. 20(2):129-136, February 1, 1999.
• Intelligent Modeling: Advances in Open Pit Mine Design and Optimization Research, International Journal of Surface Mining, Reclamation and Environment,Volume 16, Number 2 / June 2002.
• A NEURAL NETWORK MODEL OF THE SALINITY IN THE WEST PEARL RIVER ESTUARY
• Diagnosis of Sensorineural Hearing Loss with Neural Networks versus Logistic Regression Modeling of Distortion Product Otoacoustic Emissions, Audiology & Neuro-Otology. 9(2):81-87, March/April 2004.
• Geometric Constraints in Image Sequence and Neural Networks for ...
  Monetary Conditions & Core Inflation: An Application of Neural Networks
• CS Principles
• P. Salem (editor),"Neural Networks Applications in Social Science Research.", Organizational communication and change. Pp. 151-173. Cresskill, NJ: Hampton Press (1999).