Smart training in neurocomputing modeling of data rich systems

In this paper,“smart” training is introduced to increase the accuracy of neurocomputing modeling and reduce the time required to develop neurocomputing applications. It is shown that both objectives are attained by (i) partitioning the training data into contiguous subsets whose response belongs to a similar class, and (ii) designating different neural networks to fitting partitioned data, (iii) using a statistical algorithm to prescribe the accuracy of approximations, and (iv) employing genetic processing to initialize a network for training. It is demonstrated that all non-trivial decision-making required to automate the training can be handled using a fuzzy set theory approach. The smart training is applied to a complicated, highly non-linear 3-dimensional field represented by about forty thousand discrete points to demonstrate the applicability of the approach to data rich systems.