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Batch training

Care must be taken when using batch type algorithms, like QP, CG, SCG and Rprop. These algorithms depend heavily on changes in the error value between consecutive positions. Consequently, it is important that the same patterns are used for consecutive updates unless very large data samples are used so that fluctuations are negligible. This is done in JETNET 3.0 by setting MSTJN(2) equal to the total number of training exemplars and MSTJN(9) equal to one. This ensures that JETNET 3.0 will be evaluating the correct error function all the time.

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Fri Feb 24 11:28:59 MET 1995