TY  - JOUR
AB  - 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.
DA  - 2013
DO  - 10.1142/S021848851340014X
KW  - CoR-Lab Publication
KW  - Extreme learning machine
KW  - neural network
KW  - prior knowledge
KW  - continuous constraints
KW  - regression
LA  - eng
IS  - Suppl 2
M2  - 35
PY  - 2013
SN  - 0218-4885
SP  - 35-50
T2  - International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
TI  - Reliable Integration of Continuous Constraints into Extreme Learning Machines
UR  - https://nbn-resolving.org/urn:nbn:de:0070-pub-25479090
Y2  - 2024-11-22T03:27:40
ER  -