Neural 2.00 — A program for neural net and statistical pattern recognition Original Research Article

A neural net program for pattern classification is presented, which includes: i) an improved version of Kohonenʹs learning vector quantization (LVQ with training count); ii) feed-forward neural networks with back-propagation training; iii) Gaussian (or Mahalanobis distance) classification; iv) Fisher linear discrimination. Back-prop trainings with emulations of Intelʹs ETANN and Siemensʹ MA16 neural chips are available as options. The program has been developed for high energy physics applications.