The dependence identification neural network construction algorithm.

ABSTRACT

An algorithm for constructing and training multilayer neural networks, dependence identification, is presented in this paper. Its distinctive features are that (i) it transforms the training problem into a set of quadratic optimization problems that are solved by a number of linear equations, (ii) it constructs an appropriate network to meet the training specifications, and (iii) the resulting network architecture and weights can be further refined with standard training algorithms, like backpropagation, giving a significant speedup in the development time of the neural network and decreasing the amount of trial and error usually associated with network development.