The dependence identification neural network construction algorithm

An algorithm for constructing and training multilayer neural networks, dependence identification, is presented in this paper. Its distinctive features are that: 1) it transforms the training problem into a set of quadratic optimization problems that are solved by a number of linear equations; and 2) it constructs an appropriate network to meet the training specifications. The architecture and network weights produced by the algorithm can also be used as initial conditions for further online training by backpropagation or a similar iterative gradient descent training algorithm if necessary. In addition to constructing an appropriate network based on training data, the dependence identification algorithm significantly speeds up learning in feedforward multilayer neural networks compared to standard backpropagation.<
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