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 on Windows versions 7 and higher.
Some of the features of WinNN:
- WinNN32 is a fast 32-bit implementation that tested to run on Windows 95/98/NT/2000/XP/Me/Vista/WIndows 7, 8, 8.1 and 10
- Smoothly trains in the background. Supports DDE with Excel (use NN predictions in Excel).
- Supports DDE with Excel (train and use NN predictions in Excel).
- Builtin 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.
- WinNN is available 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).
A screen shot of the main windows of WinNN
