The Optimal Interpolative Net

Dan Simon
Department
of Electrical Engineering
Cleveland State University

Cleveland, Ohio
 

The Optimal Interpolative (OI) Net is a three-layer classification neural network that grows middle layer neurons during training. The network was developed by Professor deFigueiredo and his associates, and a good overview of the architecture and training algorithm can be found in [1]. More recently, Professor Simon has extended the learning algorithm to include distributed fault tolerance [2]. This web page makes available the classical Iris data that can be used to test the OI Net, along with various m-files that can be run in the MATLAB environment. M-files are written in a very high-level language that can be easily read, almost like pseudo code. The data files and m-files are contained in the following zip file.

OINet.zip - 15 kilobytes

If you download OINet.zip to your hard drive by clicking on the above link, then unzip the file (using, for example, WinZip), you can run OI Net experiments and reproduce the results in reference [2]. When you unzip the file on your hard drive, look at the readme.txt file for more detailed information. If you don't have software to unzip the file, you can download a free evaluation version of WinZip from www.winzip.com.

References

  1. S. Sin and R. deFigueiredo, “An evolution-oriented learning algorithm for the optimal interpolative net,” IEEE Transactions on Neural Networks, volume 3, number 2, pp. 315-323, March 1992.
  2. D. Simon, “Distributed Fault Tolerance in Optimal Interpolative Nets,” IEEE Transactions on Neural Networks, vol. 12, no. 6, pp. 1348-1357, November 2001 - pdf, 174 KB

Professor Simon's Home Page

Department of Electrical and Computer Engineering

Cleveland State University


Last Revised: December 13, 2013