Superior training of artificial neural networks using weight-space partitioning

摘要

Linear least squares simplex (LLSSIM) is a new algorithm for batch training of three-layer feedforward artificial neural networks (ANN), based on a partitioning of the weight space. The input-hidden weights are trained using a "multi-start downhill simplex" global search algorithm, and the hidden-output weights are estimated using "conditional linear least squares". Monte-Carlo testing shows that LLSSIM provides globally superior weight estimates with significantly fewer function evaluations than the conventional backpropagation, adaptive backpropagation, and conjugate gradient strategies.