The application of machine learning methods in the engineering of intelligent technical

systems often requires the integration of continuous constraints like positivity, mono-

tonicity, or bounded curvature in the learned function to guarantee a reliable perfor-

mance. We show that the extreme learning machine is particularly well suited for this

task. Constraints involving arbitrary derivatives of the learned function are effectively

implemented through quadratic optimization because the learned function is linear in its

parameters, and derivatives can be derived analytically. We further provide a construc-

tive approach to verify that discretely sampled constraints are generalized to continuous

regions and show how local violations of the constraint can be rectified by iterative re-

learning. We demonstrate the approach on a practical and challenging control problem

from robotics, illustrating also how the proposed method enables learning from few data

samples if additional prior knowledge about the problem is available.