Reliable Integration of Continuous Constraints into Extreme Learning Machines

Neumann K, Rolf M, Steil JJ (2013)
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 21(Suppl 2): 35-50.

Zeitschriftenaufsatz | Veröffentlicht | Englisch
 
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Abstract / Bemerkung
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.
Stichworte
CoR-Lab Publication; Extreme learning machine; neural network; prior knowledge; continuous constraints; regression
Erscheinungsjahr
2013
Zeitschriftentitel
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Band
21
Ausgabe
Suppl 2
Seite(n)
35-50
ISSN
0218-4885
eISSN
1793-6411
Page URI
https://pub.uni-bielefeld.de/record/2547909

Zitieren

Neumann K, Rolf M, Steil JJ. Reliable Integration of Continuous Constraints into Extreme Learning Machines. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems. 2013;21(Suppl 2):35-50.
Neumann, K., Rolf, M., & Steil, J. J. (2013). Reliable Integration of Continuous Constraints into Extreme Learning Machines. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 21(Suppl 2), 35-50. doi:10.1142/S021848851340014X
Neumann, K., Rolf, M., and Steil, J. J. (2013). Reliable Integration of Continuous Constraints into Extreme Learning Machines. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 21, 35-50.
Neumann, K., Rolf, M., & Steil, J.J., 2013. Reliable Integration of Continuous Constraints into Extreme Learning Machines. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 21(Suppl 2), p 35-50.
K. Neumann, M. Rolf, and J.J. Steil, “Reliable Integration of Continuous Constraints into Extreme Learning Machines”, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, vol. 21, 2013, pp. 35-50.
Neumann, K., Rolf, M., Steil, J.J.: Reliable Integration of Continuous Constraints into Extreme Learning Machines. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems. 21, 35-50 (2013).
Neumann, Klaus, Rolf, Matthias, and Steil, Jochen J. “Reliable Integration of Continuous Constraints into Extreme Learning Machines”. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 21.Suppl 2 (2013): 35-50.
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2019-09-06T09:18:08Z
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