  Feedforward neural networks are widely used as universal predictive models to
fit data distribution. Common gradient-based learning, however, suffers from
many drawbacks making the training process ineffective and time-consuming.
Alternative randomized learning does not use gradients but selects hidden node
parameters randomly. This makes the training process extremely fast. However,
the problem in randomized learning is how to determine the random parameters. A
recently proposed method uses autoencoders for unsupervised parameter learning.
This method showed superior performance on classification tasks. In this work,
we apply this method to regression problems, and, finding that it has some
drawbacks, we show how to improve it. We propose a learning method of
autoencoders that controls the produced random weights. We also propose how to
determine the biases of hidden nodes. We empirically compare autoencoder based
learning with other randomized learning methods proposed recently for
regression and find that despite the proposed improvement of the autoencoder
based learning, it does not outperform its competitors in fitting accuracy.
Moreover, the method is much more complex than its competitors.
