Journal
INTERNATIONAL JOURNAL OF UNCERTAINTY FUZZINESS AND KNOWLEDGE-BASED SYSTEMS
Volume 21, Issue -, Pages 35-50Publisher
WORLD SCIENTIFIC PUBL CO PTE LTD
DOI: 10.1142/S021848851340014X
Keywords
Extreme learning machine; neural network; prior knowledge; continuous constraints; regression
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Funding
- German Federal Ministry of Education and Research (BMBF) within the Leading-Edge Cluster Competition it's-owl (intelligent technical systems)
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The application of machine learning methods in the engineering of intelligent technical systems often requires the integration of continuous constraints like positivity, monotonicity, or bounded curvature in the learned function to guarantee a reliable performance. 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 constructive 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.
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