CHAPTER 3 – Systems Considerations

Publisher Summary Neural network tools (NNTs) can provide solutions for a variety of problems. For a few of these problems, no other ways are known to provide solutions. For another subset of problems, other ways to tackle them may exist; however, using an NNT is by far the easiest and/or gives the best results. For still another subset, other methods might work and could be implemented with about the same amount of work. For the last subset, there are clearly better ways to attack the problem than by using NNTs. This chapter presents guidelines regarding the evaluation of an NNT for use in a particular situation and the decision regarding the subsets that a problem falls into. This evaluation should always be done from a systems point of view. The best candidate problems for neural network analysis are those that are characterized by fuzzy, imprecise, and imperfect knowledge (data), and/or by a lack of a clearly stated mathematical algorithm for the analysis of the data. However, it is important that the data should be enough to yield sufficient training and test sets to train and evaluate the performance of an NNT effectively.